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1
Sadeq, H., J. Drummond, and Z. Li. "MERGING DIGITAL SURFACE MODELS IMPLEMENTING BAYESIAN APPROACHES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 711–18. http://dx.doi.org/10.5194/isprs-archives-xli-b7-711-2016.
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In this research different DSMs from different sources have been merged. The merging is based on a probabilistic model using a Bayesian Approach. The implemented data have been sourced from very high resolution satellite imagery sensors (e.g. WorldView-1 and Pleiades). It is deemed preferable to use a Bayesian Approach when the data obtained from the sensors are limited and it is difficult to obtain many measurements or it would be very costly, thus the problem of the lack of data can be solved by introducing a priori estimations of data. To infer the prior data, it is assumed that the roofs of the buildings are specified as smooth, and for that purpose local entropy has been implemented. In addition to the a priori estimations, GNSS RTK measurements have been collected in the field which are used as check points to assess the quality of the DSMs and to validate the merging result. The model has been applied in the West-End of Glasgow containing different kinds of buildings, such as flat roofed and hipped roofed buildings. Both quantitative and qualitative methods have been employed to validate the merged DSM. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the well established Maximum Likelihood model and showed similar quantitative statistical results and better qualitative results. Although the proposed model has been applied on DSMs that were derived from satellite imagery, it can be applied to any other sourced DSMs.
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Sadeq, H., J. Drummond, and Z. Li. "MERGING DIGITAL SURFACE MODELS IMPLEMENTING BAYESIAN APPROACHES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 711–18. http://dx.doi.org/10.5194/isprsarchives-xli-b7-711-2016.
Full textAbstract:
In this research different DSMs from different sources have been merged. The merging is based on a probabilistic model using a Bayesian Approach. The implemented data have been sourced from very high resolution satellite imagery sensors (e.g. WorldView-1 and Pleiades). It is deemed preferable to use a Bayesian Approach when the data obtained from the sensors are limited and it is difficult to obtain many measurements or it would be very costly, thus the problem of the lack of data can be solved by introducing a priori estimations of data. To infer the prior data, it is assumed that the roofs of the buildings are specified as smooth, and for that purpose local entropy has been implemented. In addition to the a priori estimations, GNSS RTK measurements have been collected in the field which are used as check points to assess the quality of the DSMs and to validate the merging result. The model has been applied in the West-End of Glasgow containing different kinds of buildings, such as flat roofed and hipped roofed buildings. Both quantitative and qualitative methods have been employed to validate the merged DSM. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the well established Maximum Likelihood model and showed similar quantitative statistical results and better qualitative results. Although the proposed model has been applied on DSMs that were derived from satellite imagery, it can be applied to any other sourced DSMs.
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Liao, Jianghua, Jinxing Zhou, and Wentao Yang. "Comparing LiDAR and SfM digital surface models for three land cover types." Open Geosciences 13, no. 1 (January 1, 2021): 497–504. http://dx.doi.org/10.1515/geo-2020-0257.
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Abstract Airborne light detection and ranging (LiDAR) and unmanned aerial vehicle structure from motion (UAV-SfM) are two major methods used to produce digital surface models (DSMs) for geomorphological studies. Previous studies have used both types of DSM datasets interchangeably and ignored their differences, whereas others have attempted to locally compare these differences. However, few studies have quantified these differences for different land cover types. Therefore, we simultaneously compared the two DSMs using airborne LiDAR and UAV-SfM for three land cover types (i.e. forest, wasteland, and bare land) in northeast China. Our results showed that the differences between the DSMs were the greatest for forest areas. Further, the average elevation of the UAV-SfM DSM was 0.4 m lower than that of the LiDAR DSM, with a 95th percentile difference of 3.62 m for the forest areas. Additionally, the average elevations of the SfM DSM for wasteland and bare land were 0.16 and 0.43 m lower, respectively, than those of the airborne LiDAR DSM; the 95th percentile differences were 0.67 and 0.64 m, respectively. The differences between the two DSMs were generally minor over areas with sparse vegetation and more significant for areas covered by tall dense trees. The findings of this research can guide the joint use of different types of DSMs in certain applications, such as land management and soil erosion studies. A comparison of the DSM types in complex terrains should be explored in the future.
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Ilehag, R., S. Auer, and P. d’Angelo. "EXPLOITATION OF DIGITAL SURFACE MODELS GENERATED FROM WORLDVIEW-2 DATA FOR SAR SIMULATION TECHNIQUES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 30, 2017): 55–61. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-55-2017.
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GeoRaySAR, an automated SAR simulator developed at DLR, identifies buildings in high resolution SAR data by utilizing geometric knowledge extracted from digital surface models (DSMs). Hitherto, the simulator has utilized DSMs generated from LiDAR data from airborne sensors with pre-filtered vegetation. Discarding the need for pre-optimized model input, DSMs generated from high resolution optical data (acquired with WorldView-2) are used for the extraction of building-related SAR image parts in this work. An automatic preprocessing of the DSMs has been developed for separating buildings from elevated vegetation (trees, bushes) and reducing the noise level. Based on that, automated simulations are triggered considering the properties of real SAR images. <br><br> Locations in three cities, Munich, London and Istanbul, were chosen as study areas to determine advantages and limitations related to WorldView-2 DSMs as input for GeoRaySAR. Beyond, the impact of the quality of the DSM in terms of building extraction is evaluated as well as evaluation of building DSM, a DSM only containing buildings. The results indicate that building extents can be detected with DSMs from optical satellite data with various success, dependent on the quality of the DSM as well as on the SAR imaging perspective.
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Pang, S., X. Hu, M. Zhang, and L. Ye. "SEMI – GLOBAL MERGING OF DIGITAL SURFACE MODELS FROM MULTIPLE STEREOPAIRS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W4 (September 13, 2017): 267–71. http://dx.doi.org/10.5194/isprs-annals-iv-2-w4-267-2017.
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The semi-global optimization algorithm, which approximates a global 2D smoothness constraint by combining several 1D constraints, has been widely used in the field of image dense matching for digital surface model (DSM) generation. However, due to occlusion, shadow and textureless area of the matching images, some inconsistency may exist in the overlapping areas of different DSMs. To address this problem, based on the DSMs generated by semi-global matching from multiple stereopairs, a novel semi-global merging algorithm is proposed to generate a reliable and consistent DSM in this paper. Two datasets, each covering 1&thinsp;km<sup>2</sup>, are used to validate the proposed method. Experimental results show that the optimal DSM after merging can effectively eliminate the inconsistency and reduce redundancy in the overlapping areas.
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Rogers, Stephanie R., Ian Manning, and William Livingstone. "Comparing the Spatial Accuracy of Digital Surface Models from Four Unoccupied Aerial Systems: Photogrammetry Versus LiDAR." Remote Sensing 12, no. 17 (August 29, 2020): 2806. http://dx.doi.org/10.3390/rs12172806.
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The technological growth and accessibility of Unoccupied Aerial Systems (UAS) have revolutionized the way geographic data are collected. Digital Surface Models (DSMs) are an integral component of geospatial analyses and are now easily produced at a high resolution from UAS images and photogrammetric software. Systematic testing is required to understand the strengths and weaknesses of DSMs produced from various UAS. Thus, in this study, we used photogrammetry to create DSMs using four UAS (DJI Inspire 1, DJI Phantom 4 Pro, DJI Mavic Pro, and DJI Matrice 210) to test the overall accuracy of DSM outputs across a mixed land cover study area. The accuracy and spatial variability of these DSMs were determined by comparing them to (1) 12 high-precision GPS targets (checkpoints) in the field, and (2) a DSM created from Light Detection and Ranging (LiDAR) (Velodyne VLP-16 Puck Lite) on a fifth UAS, a DJI Matrice 600 Pro. Data were collected on July 20, 2018 over a site with mixed land cover near Middleton, NS, Canada. The study site comprised an area of eight hectares (~20 acres) with land cover types including forest, vines, dirt road, bare soil, long grass, and mowed grass. The LiDAR point cloud was used to create a 0.10 m DSM which had an overall Root Mean Square Error (RMSE) accuracy of ±0.04 m compared to 12 checkpoints spread throughout the study area. UAS were flown three times each and DSMs were created with the use of Ground Control Points (GCPs), also at 0.10 m resolution. The overall RMSE values of UAS DSMs ranged from ±0.03 to ±0.06 m compared to 12 checkpoints. Next, DSMs of Difference (DoDs) compared UAS DSMs to the LiDAR DSM, with results ranging from ±1.97 m to ±2.09 m overall. Upon further investigation over respective land covers, high discrepancies occurred over vegetated terrain and in areas outside the extent of GCPs. This indicated LiDAR’s superiority in mapping complex vegetation surfaces and stressed the importance of a complete GCP network spanning the entirety of the study area. While UAS DSMs and LiDAR DSM were of comparable high quality when evaluated based on checkpoints, further examination of the DoDs exposed critical discrepancies across the study site, namely in vegetated areas. Each of the four test UAS performed consistently well, with P4P as the clear front runner in overall ranking.
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Krauß, T. "DERIVATION OF BUILDING STRUCTURES FROM NOISY DIGITAL SURFACE MODELS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 15–20. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-15-2023.
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Abstract. In this work we present a novel approach for segementation of a noisy DSM to building structures and other non-building structures – normally trees – and the modeling of them. Mostly Digital Surface Models (DSMs) from only a few aerial images or only from one pair of satellite images tend to be very noisy and lack good quality especially in shadow areas. Since actual methods for deriving roofs rely on a valid height information by joining areas of same slope to a roof-plane these fail regularly with such noisy DSMs. In our presented approach we use a slope map of the DSM only to detect flat regions. Since those regions on top of roofs are mostly good illuminated we can derive the ridges of roofs and flat roofs and also ground areas. All narrow, flat, elevated areas are ridges and may occur on roofs or on trees. After connecting ridges in ridge-directions there remain two types of ridges: long, straight ridges of roofs and mixed short ridges in many directions for the trees. Fitting symmetric planes through the roof-ridge-lines gives finally the roof-planes reducing the effects of noise on shadowed parts of the roof. Taking the other tree-ridges as seeds for a watershed transformation will give the trees. Finally the proposed method is applied to a noisy DSM and the results will be discussed.
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Lowe, Meagan K., Farrah Anis Fazliatul Adnan, Sarah M. Hamylton, Rafael C. Carvalho, and Colin D. Woodroffe. "Assessing Reef-Island Shoreline Change Using UAV-Derived Orthomosaics and Digital Surface Models." Drones 3, no. 2 (May 14, 2019): 44. http://dx.doi.org/10.3390/drones3020044.
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This study presents an analysis of shoreline change on reef islands using unmanned aerial vehicle (UAV)-derived orthomosaics and digital surface models (DSMs) collected on Sipadan Island, Sabah, Malaysia, and Sasahura Ite Island, Isabel Province, Solomon Islands. The high resolution of UAV-derived orthomosaics enabled changes in the position of the base of beach to be detected with confidence. The accuracy of the UAV-derived DSMs was assessed against equivalent topographic profiles via root-mean-square error, and found to be <0.21 m in all but one case; this demonstrates the potential for using UAV-derived DSMs to interpret three-dimensional island beach morphology and detect patterns of geomorphic change. The correlation between planimetric and volumetric change along selected beach transects was also investigated and found to be variable, indicating that a multifaceted approach including both planimetric (two-dimensional) and volumetric (three-dimensional) metrics is of value when analysing reef-island change. However, interpretations of UAV-derived data must carefully consider errors associated with global positioning system (GPS) positioning, the distribution of ground control points, the chosen UAV flight parameters, and the data processing methodology. Further application of this technology has the potential to expand our understanding of reef-island morphodynamics and their vulnerability to sea-level rise and other stressors.
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Wang, Dejiang, Quanming Jiang, and Jinzheng Liu. "Deep-Learning-Based Automated Building Information Modeling Reconstruction Using Orthophotos with Digital Surface Models." Buildings 14, no. 3 (March 15, 2024): 808. http://dx.doi.org/10.3390/buildings14030808.
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In the field of building information modeling (BIM), converting existing buildings into BIM by using orthophotos with digital surface models (DSMs) is a critical technical challenge. Currently, the BIM reconstruction process is hampered by the inadequate accuracy of building boundary extraction when carried out using existing technology, leading to insufficient correctness in the final BIM reconstruction. To address this issue, this study proposes a novel deep-learning- and postprocessing-based approach to automating reconstruction in BIM by using orthophotos with DSMs. This approach aims to improve the efficiency and correctness of the reconstruction of existing buildings in BIM. The experimental results in the publicly available Tianjin and Urban 3D reconstruction datasets showed that this method was able to extract accurate and regularized building boundaries, and the correctness of the reconstructed BIM was 85.61% and 82.93%, respectively. This study improved the technique of extracting regularized building boundaries from orthophotos and DSMs and achieved significant results in enhancing the correctness of BIM reconstruction. These improvements are helpful for the reconstruction of existing buildings in BIM, and this study provides a solid foundation for future improvements to the algorithm.
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Noh, Myoung-Jong, and Ian M. Howat. "Analysis of PlanetScope Dove Digital Surface Model Accuracy Using Geometrically Simulated Images." Remote Sensing 15, no. 14 (July 11, 2023): 3496. http://dx.doi.org/10.3390/rs15143496.
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Many objectives in geoscience and engineering require Earth surface elevations at greater temporospatial resolution and coverage than are currently available. This may be achieved with stereo imagery from large constellations of “small sats”, such as PlanetScope Doves. Obtaining Digital Surface Models (DSMs) of sufficient quality from these images is challenging due to their lower resolution and weaker stereo geometry relative to stereo mode satellites such as WorldView. The quality can be improved by utilizing their much larger numbers of repeat images, but this requires effective stereopair selection. To determine the stereo geometries required for obtaining quality DSMs from PlanetScope Dove imagery, we apply a new methodology for generating simulated stereo images of varying geometries using adjusted orientation parameters obtained by a self-calibrating bundle adjustment and validated by comparing the resulting rigorous sensor and rational function models. The accuracies of simulated stereo and multi-pair DSMs are then assessed through comparison to a reference DSM, providing the relationship between specific imaging geometries and DSM quality. Our results provide a basis for automated stereo imagery selection to enable large-scale DSM production from PlanetScope Dove imagery. Our methodology can be applied to other sources of stereo imagery and designing future satellite missions. In the future, we will further develop multi-pair matching algorithms for generating DSMs with Dove Classic images to improve both accuracy and quality that are otherwise limited by the weak stereo geometry of single stereo pairs.
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Bittner, Ksenia, Pablo d’Angelo, Marco Körner, and Peter Reinartz. "DSM-to-LoD2: Spaceborne Stereo Digital Surface Model Refinement." Remote Sensing 10, no. 12 (November 30, 2018): 1926. http://dx.doi.org/10.3390/rs10121926.
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A digital surface model (DSM) provides the geometry and structure of an urban environment with buildings being the most prominent objects in it. Built-up areas change with time due to the rapid expansion of cities. New buildings are being built, existing ones are expanded, and old buildings are torn down. As a result, 3D surface models can increase the understanding and explanation of complex urban scenarios. They are very useful in numerous fields of remote sensing applications, in tasks related to 3D reconstruction and city modeling, planning, visualization, disaster management, navigation, and decision-making, among others. DSMs are typically derived from various acquisition techniques, like photogrammetry, laser scanning, or synthetic aperture radar (SAR). The generation of DSMs from very high resolution optical stereo satellite imagery leads to high resolution DSMs which often suffer from mismatches, missing values, or blunders, resulting in coarse building shape representation. To overcome these problems, we propose a method for 3D surface model generation with refined building shapes to level of detail (LoD) 2 from stereo half-meter resolution satellite DSMs using deep learning techniques. Mainly, we train a conditional generative adversarial network (cGAN) with an objective function based on least square residuals to generate an accurate LoD2-like DSM with enhanced 3D object shapes directly from the noisy stereo DSM input. In addition, to achieve close to LoD2 shapes of buildings, we introduce a new approach to generate an artificial DSM with accurate and realistic building geometries from city geography markup language (CityGML) data, on which we later perform a training of the proposed cGAN architecture. The experimental results demonstrate the strong potential to create large-scale remote sensing elevation models where the buildings exhibit better-quality shapes and roof forms than just using the matching process. Moreover, the developed model is successfully applied to a different city that is unseen during the training to show its generalization capacity.
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MARUYAMA, YOSHIHISA, AKIRA TASHIRO, and FUMIO YAMAZAKI. "DETECTION OF COLLAPSED BUILDINGS DUE TO EARTHQUAKES USING A DIGITAL SURFACE MODEL CONSTRUCTED FROM AERIAL IMAGES." Journal of Earthquake and Tsunami 08, no. 01 (March 2014): 1450003. http://dx.doi.org/10.1142/s1793431114500031.
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The buildings that collapsed during the 2007 Niigata Chuetsu-oki earthquake are detected based on aerial photogrammetry using digital aerial images. The digital surface models (DSMs) in the area where severe damage to buildings was observed after the earthquake are constructed using digital aerial camera images. Pre- and post-event aerial images are employed to obtain the DSMs in this study. The differences in building heights between the pre- and post-event models are considered to detect collapsed buildings and the accuracy of the method is discussed in this paper. The results indicate that the collapsed buildings can be detected and undamaged buildings can also be correctly recognized by the proposed method.
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Kim, Soohyeon, Sooahm Rhee, and Taejung Kim. "Digital Surface Model Interpolation Based on 3D Mesh Models." Remote Sensing 11, no. 1 (December 24, 2018): 24. http://dx.doi.org/10.3390/rs11010024.
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A digital surface model (DSM) is an important geospatial infrastructure used in various fields. In this paper, we deal with how to improve the quality of DSMs generated from stereo image matching. During stereo image matching, there are outliers due to mismatches, and non-matching regions due to match failure. Such outliers and non-matching regions have to be corrected accurately and efficiently for high-quality DSM generation. This process has been performed by applying a local distribution model, such as inverse distance weight (IDW), or by forming a triangulated irregular network (TIN). However, if the area of non-matching regions is large, it is not trivial to interpolate elevation values using neighboring cells. In this study, we proposed a new DSM interpolation method using a 3D mesh model, which is more robust to outliers and large holes. We compared mesh-based DSM with IDW-based DSM and analyzed the characteristics of each. The accuracy of the mesh-based DSM was a 2.80 m root mean square error (RMSE), while that for the IDW-based DSM was 3.22 m. While the mesh-based DSM successfully removed empty grid cells and outliers, the IDW-based DSM had sharper object boundaries. Because of the nature of surface reconstruction, object boundaries appeared smoother on the mesh-based DSM. We further propose a method of integrating the two DSMs. The integrated DSM maintains the sharpness of object boundaries without significant accuracy degradation. The contribution of this paper is the use of 3D mesh models (which have mainly been used for 3D visualization) for efficient removal of outliers and non-matching regions without a priori knowledge of surface types.
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Breytenbach, A. "COMPARATIVE ACCURACY EVALUATION OF FINE-SCALE GLOBAL AND LOCAL DIGITAL SURFACE MODELS: THE TSHWANE CASE STUDY I." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W1 (October 5, 2016): 211–22. http://dx.doi.org/10.5194/isprs-annals-iv-2-w1-211-2016.
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Conducted in the City of Tshwane, South Africa, this study set about to test the accuracy of DSMs derived from different remotely sensed data locally. VHR digital mapping camera stereo-pairs, tri-stereo imagery collected by a Pléiades satellite and data detected from the Tandem-X InSAR satellite configuration were fundamental in the construction of seamless DSM products at different postings, namely 2 m, 4 m and 12 m. The three DSMs were sampled against independent control points originating from validated airborne LiDAR data. The reference surfaces were derived from the same dense point cloud at grid resolutions corresponding to those of the samples. The absolute and relative positional accuracies were computed using well-known DEM error metrics and accuracy statistics. Overall vertical accuracies were also assessed and compared across seven slope classes and nine primary land cover classes. Although all three DSMs displayed significantly more vertical errors where solid waterbodies, dense natural and/or alien woody vegetation and, in a lesser degree, urban residential areas with significant canopy cover were encountered, all three surpassed their expected positional accuracies overall.
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de Roos, Shannon, Darren Turner, Arko Lucieer, and David M. J. S. Bowman. "Using Digital Surface Models from UAS Imagery of Fire Damaged Sphagnum Peatlands for Monitoring and Hydrological Restoration." Drones 2, no. 4 (December 14, 2018): 45. http://dx.doi.org/10.3390/drones2040045.
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The sub-alpine and alpine Sphagnum peatlands in Australia are geographically constrained to poorly drained areas c. 1000 m a.s.l. Sphagnum is an important contributor to the resilience of peatlands; however, it is also very sensitive to fire and often shows slow recovery after being damaged. Recovery is largely dependent on a sufficient water supply and impeded drainage. Monitoring the fragmented areas of Australia’s peatlands can be achieved by capturing ultra-high spatial resolution imagery from an unmanned aerial systems (UAS). High resolution digital surface models (DSMs) can be created from UAS imagery, from which hydrological models can be derived to monitor hydrological changes and assist with rehabilitation of damaged peatlands. One of the constraints of the use of UAS is the intensive fieldwork required. The need to distribute ground control points (GCPs) adds to fieldwork complexity. GCPs are often used for georeferencing of the UAS imagery, as well as for removal of artificial tilting and doming of the photogrammetric model created by camera distortions. In this study, Tasmania’s northern peatlands were mapped to test the viability of creating hydrological models. The case study was further used to test three different GCP scenarios to assess the effect on DSM quality. From the five scenarios, three required the use of all (16–20) GCPs to create accurate DSMs, whereas the two other sites provided accurate DSMs when only using four GCPs. Hydrological maps produced with the TauDEM tools software package showed high visual accuracy and a good potential for rehabilitation guidance, when using ground- controlled DSMs.
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Gong, K., and D. Fritsch. "A DETAILED STUDY ABOUT DIGITAL SURFACE MODEL GENERATION USING HIGH RESOLUTION SATELLITE STEREO IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-1 (June 1, 2016): 69–76. http://dx.doi.org/10.5194/isprsannals-iii-1-69-2016.
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Photogrammetry is currently in a process of renaissance, caused by the development of dense stereo matching algorithms to provide very dense Digital Surface Models (DSMs). Moreover, satellite sensors have improved to provide sub-meter or even better Ground Sampling Distances (GSD) in recent years. Therefore, the generation of DSM from spaceborne stereo imagery becomes a vivid research area. This paper presents a comprehensive study about the DSM generation of high resolution satellite data and proposes several methods to implement the approach. The bias-compensated Rational Polynomial Coefficients (RPCs) Bundle Block Adjustment is applied to image orientation and the rectification of stereo scenes is realized based on the Project-Trajectory-Based Epipolarity (PTE) Model. Very dense DSMs are generated from WorldView-2 satellite stereo imagery using the dense image matching module of the C/C++ library LibTsgm. We carry out various tests to evaluate the quality of generated DSMs regarding robustness and precision. The results have verified that the presented pipeline of DSM generation from high resolution satellite imagery is applicable, reliable and very promising.
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Gong, K., and D. Fritsch. "A DETAILED STUDY ABOUT DIGITAL SURFACE MODEL GENERATION USING HIGH RESOLUTION SATELLITE STEREO IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-1 (June 1, 2016): 69–76. http://dx.doi.org/10.5194/isprs-annals-iii-1-69-2016.
Full textAbstract:
Photogrammetry is currently in a process of renaissance, caused by the development of dense stereo matching algorithms to provide very dense Digital Surface Models (DSMs). Moreover, satellite sensors have improved to provide sub-meter or even better Ground Sampling Distances (GSD) in recent years. Therefore, the generation of DSM from spaceborne stereo imagery becomes a vivid research area. This paper presents a comprehensive study about the DSM generation of high resolution satellite data and proposes several methods to implement the approach. The bias-compensated Rational Polynomial Coefficients (RPCs) Bundle Block Adjustment is applied to image orientation and the rectification of stereo scenes is realized based on the Project-Trajectory-Based Epipolarity (PTE) Model. Very dense DSMs are generated from WorldView-2 satellite stereo imagery using the dense image matching module of the C/C++ library LibTsgm. We carry out various tests to evaluate the quality of generated DSMs regarding robustness and precision. The results have verified that the presented pipeline of DSM generation from high resolution satellite imagery is applicable, reliable and very promising.
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Grottoli, Edoardo, Mélanie Biausque, David Rogers, Derek W. T. Jackson, and J. Andrew G. Cooper. "Structure-from-Motion-Derived Digital Surface Models from Historical Aerial Photographs: A New 3D Application for Coastal Dune Monitoring." Remote Sensing 13, no. 1 (December 30, 2020): 95. http://dx.doi.org/10.3390/rs13010095.
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Recent advances in structure-from-motion (SfM) techniques have proliferated the use of unmanned aerial vehicles (UAVs) in the monitoring of coastal landform changes, particularly when applied in the reconstruction of 3D surface models from historical aerial photographs. Here, we explore a number of depth map filtering and point cloud cleaning methods using the commercial software Agisoft Metashape Pro to determine the optimal methodology to build reliable digital surface models (DSMs). Twelve different aerial photography-derived DSMs are validated and compared against light detection and ranging (LiDAR)- and UAV-derived DSMs of a vegetated coastal dune system that has undergone several decades of coastline retreat. The different studied methods showed an average vertical error (root mean square error, RMSE) of approximately 1 m, with the best method resulting in an error value of 0.93 m. In our case, the best method resulted from the removal of confidence values in the range of 0–3 from the dense point cloud (DPC), with no filter applied to the depth maps. Differences among the methods examined were associated with the reconstruction of the dune slipface. The application of the modern SfM methodology to the analysis of historical aerial (vertical) photography is a novel (and reliable) new approach that can be used to better quantify coastal dune volume changes. DSMs derived from suitable historical aerial photographs, therefore, represent dependable sources of 3D data that can be used to better analyse long-term geomorphic changes in coastal dune areas that have undergone retreat.
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Persad, Ravi Ancil, and Costas Armenakis. "CO-REGISTRATION OF DSMs GENERATED BY UAV AND TERRESTRIAL LASER SCANNING SYSTEMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 985–90. http://dx.doi.org/10.5194/isprs-archives-xli-b1-985-2016.
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An approach for the co-registration of Digital Surface Models (DSMs) derived from Unmanned Aerial Vehicles (UAVs) and Terrestrial Laser Scanners (TLS) is proposed. Specifically, a wavelet-based feature descriptor for matching surface keypoints on the 2.5D DSMs is developed. DSMs are useful in wide-scope of various applications such as 3D building modelling and reconstruction, cultural heritage, urban and environmental planning, aircraft navigation/path routing, accident and crime scene reconstruction, mining as well as, topographic map revision and change detection. For these listed applications, it is not uncommon that there will be a need for automatically aligning multi-temporal DSMs which may have been acquired from multiple sensors, with different specifications over a period of time, and may have various overlaps. Terrestrial laser scanners usually capture urban facades in an accurate manner; however this is not the case for building roof structures. On the other hand, vertical photography from UAVs can capture the roofs. Therefore, the automatic fusion of UAV and laser-scanning based DSMs is addressed here as it serves various geospatial applications.
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Persad, Ravi Ancil, and Costas Armenakis. "CO-REGISTRATION OF DSMs GENERATED BY UAV AND TERRESTRIAL LASER SCANNING SYSTEMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 985–90. http://dx.doi.org/10.5194/isprsarchives-xli-b1-985-2016.
Full textAbstract:
An approach for the co-registration of Digital Surface Models (DSMs) derived from Unmanned Aerial Vehicles (UAVs) and Terrestrial Laser Scanners (TLS) is proposed. Specifically, a wavelet-based feature descriptor for matching surface keypoints on the 2.5D DSMs is developed. DSMs are useful in wide-scope of various applications such as 3D building modelling and reconstruction, cultural heritage, urban and environmental planning, aircraft navigation/path routing, accident and crime scene reconstruction, mining as well as, topographic map revision and change detection. For these listed applications, it is not uncommon that there will be a need for automatically aligning multi-temporal DSMs which may have been acquired from multiple sensors, with different specifications over a period of time, and may have various overlaps. Terrestrial laser scanners usually capture urban facades in an accurate manner; however this is not the case for building roof structures. On the other hand, vertical photography from UAVs can capture the roofs. Therefore, the automatic fusion of UAV and laser-scanning based DSMs is addressed here as it serves various geospatial applications.
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Bittner, K., P. d’Angelo, M. Körner, and P. Reinartz. "AUTOMATIC LARGE-SCALE 3D BUILDING SHAPE REFINEMENT USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 103–8. http://dx.doi.org/10.5194/isprs-archives-xlii-2-103-2018.
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<p><strong>Abstract.</strong> Three-dimensional building reconstruction from remote sensing imagery is one of the most difficult and important 3D modeling problems for complex urban environments. The main data sources provided the digital representation of the Earths surface and related natural, cultural, and man-made objects of the urban areas in remote sensing are the <i>digital surface models (DSMs)</i>. The DSMs can be obtained either by <i>light detection and ranging (LIDAR)</i>, SAR interferometry or from stereo images. Our approach relies on automatic global 3D building shape refinement from stereo DSMs using deep learning techniques. This refinement is necessary as the DSMs, which are extracted from image matching point clouds, suffer from occlusions, outliers, and noise. Though most previous works have shown promising results for building modeling, this topic remains an open research area. We present a new methodology which not only generates images with continuous values representing the elevation models but, at the same time, enhances the 3D object shapes, buildings in our case. Mainly, we train a <i>conditional generative adversarial network (cGAN)</i> to generate accurate LIDAR-like DSM height images from the noisy stereo DSM input. The obtained results demonstrate the strong potential of creating large areas remote sensing depth images where the buildings exhibit better-quality shapes and roof forms.</p>
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Re, C., S. Robson, R. Roncella, and M. Hess. "Metric Accuracy Evaluation of Dense Matching Algorithms in Archeological Applications." Geoinformatics FCE CTU 6 (December 21, 2011): 275–82. http://dx.doi.org/10.14311/gi.6.34.
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In the cultural heritage field the recording and documentation of small and medium size objects with very detailed Digital Surface Models (DSM) is readily possible by through the use of high resolution and high precision triangulation laser scanners. 3D surface recording of archaeological objects can be easily achieved in museums; however, this type of record can be quite expensive. In many cases photogrammetry can provide a viable alternative for the generation of DSMs. The photogrammetric procedure has some benefits with respect to laser survey. The research described in this paper sets out to verify the reconstruction accuracy of DSMs of some archaeological artifacts obtained by photogrammetric survey. The experimentation has been carried out on some objects preserved in the Petrie Museum of Egyptian Archaeology at University College London (UCL). DSMs produced by two photogrammetric software packages are compared with the digital 3D model obtained by a state of the art triangulation color laser scanner. Intercomparison between the generated DSM has allowed an evaluation of metric accuracy of the photogrammetric approach applied to archaeological documentation and of precision performances of the two software packages.
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Hingee, Kassel, Peter Caccetta, Louis Caccetta, Xiaoliang Wu, and Drew Devereaux. "DIGITAL TERRAIN FROM A TWO-STEP SEGMENTATION AND OUTLIER-BASED ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 233–39. http://dx.doi.org/10.5194/isprs-archives-xli-b3-233-2016.
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We present a novel ground filter for remotely sensed height data. Our filter has two phases: the first phase segments the DSM with a slope threshold and uses gradient direction to identify candidate ground segments; the second phase fits surfaces to the candidate ground points and removes outliers. Digital terrain is obtained by a surface fit to the final set of ground points. We tested the new algorithm on digital surface models (DSMs) for a 9600<i>km</i><sup>2</sup> region around Perth, Australia. This region contains a large mix of land uses (urban, grassland, native forest and plantation forest) and includes both a sandy coastal plain and a hillier region (elevations up to 0.5km). The DSMs are captured annually at 0.2<i>m</i> resolution using aerial stereo photography, resulting in 1.2TB of input data per annum. Overall accuracy of the filter was estimated to be 89.6% and on a small semi-rural subset our algorithm was found to have 40% fewer errors compared to Inpho’s Match-T algorithm.
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Hingee, Kassel, Peter Caccetta, Louis Caccetta, Xiaoliang Wu, and Drew Devereaux. "DIGITAL TERRAIN FROM A TWO-STEP SEGMENTATION AND OUTLIER-BASED ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 233–39. http://dx.doi.org/10.5194/isprsarchives-xli-b3-233-2016.
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We present a novel ground filter for remotely sensed height data. Our filter has two phases: the first phase segments the DSM with a slope threshold and uses gradient direction to identify candidate ground segments; the second phase fits surfaces to the candidate ground points and removes outliers. Digital terrain is obtained by a surface fit to the final set of ground points. We tested the new algorithm on digital surface models (DSMs) for a 9600<i>km</i><sup>2</sup> region around Perth, Australia. This region contains a large mix of land uses (urban, grassland, native forest and plantation forest) and includes both a sandy coastal plain and a hillier region (elevations up to 0.5km). The DSMs are captured annually at 0.2<i>m</i> resolution using aerial stereo photography, resulting in 1.2TB of input data per annum. Overall accuracy of the filter was estimated to be 89.6% and on a small semi-rural subset our algorithm was found to have 40% fewer errors compared to Inpho’s Match-T algorithm.
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Zhang, Xin, Baorong Xie, Shijie Liu, Xiaohua Tong, Rongli Ding, Huan Xie, and Zhonghua Hong. "A Two-Step Block Adjustment Method for DSM Accuracy Improvement with Elevation Control of ICESat-2 Data." Remote Sensing 14, no. 18 (September 7, 2022): 4455. http://dx.doi.org/10.3390/rs14184455.
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Digital surface models (DSMs) have been widely utilized in various applications as fundamental geographic information data. Block adjustment is normally performed on satellite images to enhance the geometric accuracy and DSMs are then generated by stereo mapping. However, new errors may be introduced during the stereo mapping processing and geometric discrepancies between DSMs may still exist. In particular, block adjustment is difficult for multisource satellite images. Therefore, this paper presents a two-step block adjustment approach directly performed on DSMs, with high-accuracy ICESat-2 laser altimetry data used as elevation control. In the method, DSM tie-point matching, elevation control/check point selection from ICESat-2 laser points, and planar and elevation block adjustments are performed in sequence. In the experiments, ZY-3 satellite stereo images and corresponding generated DSMs, as well as SRTM and ALOS DSMs, are used for verification. The experimental results show that the absolute elevation accuracy and the relative geometric consistency of the DSMs are both significantly improved after two-step DSM block adjustment and it can efficiently improve the accuracy, not only for DSMs acquired by the same sensor type, but also for DSMs acquired by different sensor types, which demonstrates the feasibility and advantage of the proposed method.
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Breytenbach, Andrè. "3D Evaluation of fine-scale normalised DSMs in urban settings." South African Journal of Geomatics 9, no. 2 (September 7, 2022): 397–414. http://dx.doi.org/10.4314/sajg.v9i2.26.
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Humankind often needs to accurately model, identify and spatially quantify aboveground phenomena on the Earth’s surface for informed decision-making. Height data derived from digital elevation models (DEMs) is often used to achieve this. This study conducted a deterministic assessment of three normalised digital surface models (nDSMs) of different spatial resolutions, namely 2m, 4m and 12m, derived from VHR digital stereo aerial photography, tri-stereo Pléiades imagery and Tandem-X InSAR data, respectively. Covering a predominantly built-up area within a city landscape, the nDSMs were vertically and volumetrically compared to assess their quality and fit-for-use. In each case a consistent systematic evaluation was accomplished against a lidar derived reference surface at matching spatial resolutions (co-registered) using a semi-automated GIS routine. The relative height and volumetric errors were statistically analysed and described, including those computed individually over nine urban land cover/land use (LCLU) classes and several selected large buildings. Higher vertical accuracies were reported across single storey structures and areas with no to little or short vegetation, as apposed to substantially lower accuracies obtained over multi-levelled buildings and tall (dense) woody vegetation. Here significant underestimations of volumes exacerbated by lower spatial resolutions were also observed across each nDSM. Conversely, notable volume overestimations were found over predominantly grass-covered areas in especially the finer-scaled nDSMs. VHR elevation data is recommended to model and quantify aboveground elements spatially in 3D (e.g. buildings, earthworks and woody vegetation) in urban landscapes, but a sensitivity test beforehand remains critical to ensure more reliable outcomes for users and stakeholders alike.
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Ye, Jiang, Yuxuan Qiang, Rui Zhang, Xinguo Liu, Yixin Deng, and Jiawei Zhang. "High-Precision Digital Surface Model Extraction from Satellite Stereo Images Fused with ICESat-2 Data." Remote Sensing 14, no. 1 (December 29, 2021): 142. http://dx.doi.org/10.3390/rs14010142.
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The lack of ground control points (GCPs) affects the elevation accuracy of digital surface models (DSMs) generated by optical satellite stereo images and limits the application of high-resolution DSMs. It is a feasible idea to use ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) laser altimetry data to improve the elevation accuracy of optical stereo images, but it is necessary to accurately match the two types of data. This paper proposes a DSM registration strategy based on terrain similarity (BOTS), which integrates ICESat-2 laser altimetry data without GCPs and improves the DSM elevation accuracy generation from optical satellite stereo pairs. Under different terrain conditions, Worldview-2, SV-1, GF-7, and ZY-3 stereo pairs were used to verify the effectiveness of this method. The experimental results show that the BOTS method proposed in this paper is more robust when there are a large number of abnormal points in the ICESat-2 data or there is a large elevation gap between DSMs. After fusion of ICESat-2 data, the DSM elevation accuracy extracted from the satellite stereo pair is improved by 73~92%, and the root mean square error (RMSE) of Worldview-2 DSM reaches 0.71 m.
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Gong, K., and D. Fritsch. "POINT CLOUD AND DIGITAL SURFACE MODEL GENERATION FROM HIGH RESOLUTION MULTIPLE VIEW STEREO SATELLITE IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 363–70. http://dx.doi.org/10.5194/isprs-archives-xlii-2-363-2018.
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Nowadays, multiple-view stereo satellite imagery has become a valuable data source for digital surface model generation and 3D reconstruction. In 2016, a well-organized multiple view stereo publicly benchmark for commercial satellite imagery has been released by the John Hopkins University Applied Physics Laboratory, USA. This benchmark motivates us to explore the method that can generate accurate digital surface models from a large number of high resolution satellite images. In this paper, we propose a pipeline for processing the benchmark data to digital surface models. As a pre-procedure, we filter all the possible image pairs according to the incidence angle and capture date. With the selected image pairs, the relative bias-compensated model is applied for relative orientation. After the epipolar image pairs’ generation, dense image matching and triangulation, the 3D point clouds and DSMs are acquired. The DSMs are aligned to a quasi-ground plane by the relative bias-compensated model. We apply the median filter to generate the fused point cloud and DSM. By comparing with the reference LiDAR DSM, the accuracy, the completeness and the robustness are evaluated. The results show, that the point cloud reconstructs the surface with small structures and the fused DSM generated by our pipeline is accurate and robust.
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Hattula, Emilia, Lingli Zhu, and Jere Raninen. "Building extraction in urban and rural areas with aerial and LiDAR DSM." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W4-2024 (May 31, 2024): 73–79. http://dx.doi.org/10.5194/isprs-annals-x-4-w4-2024-73-2024.
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Abstract. Automatizing the extraction of different objects from remote sensing data with deep learning methods has been a popular research topic. Buildings have been one of those popular objects to be extracted. Not only does the selection of neural network affect the results and accuracy of extracted buildings, but also the selection of different types of data for the task. Digital surface models (DSMs) are increasingly used in remote sensing and their demand has increased. Retrieving height information from surface models has proved helpful for accurate extraction of buildings. In this study was investigated, if the use of light detection and ranging (LiDAR) DSMs and DEMs with 25 cm pixel resolution will lead to more accurate building extraction results in comparison to the use of aerial DSMs. Results with UNet models trained with building vector labels, DSMs, DEMs and true orthophotos from multiple areas of Finland, were produced with different data combinations from two Finnish cities, Savonlinna and Pudasj¨arvi, to see, which combination would lead to the most accurate building detection results. Results were evaluated partly by visual inspection, and partly by quantitative assessment. Based on the tests carried out, combining the information from true orthophotos with LiDAR DSMs and 25 cm DEMs provided the most accurate results. In forest area, using LiDAR data increased the accuracy of building detection. However, in urban area, due to missing buildings from LiDAR data, its advantages were compromised. We suggest that the use of both imagery and LiDAR data should be the optimal solution.
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Bittner, Ksenia, Marco Körner, Friedrich Fraundorfer, and Peter Reinartz. "Multi-Task cGAN for Simultaneous Spaceborne DSM Refinement and Roof-Type Classification." Remote Sensing 11, no. 11 (May 28, 2019): 1262. http://dx.doi.org/10.3390/rs11111262.
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Various deep learning applications benefit from multi-task learning with multiple regression and classification objectives by taking advantage of the similarities between individual tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models compared to separately trained models. In this paper, we make an observation of such influences for important remote sensing applications like elevation model generation and semantic segmentation tasks from the stereo half-meter resolution satellite digital surface models (DSMs). Mainly, we aim to generate good-quality DSMs with complete, as well as accurate level of detail (LoD)2-like building forms and to assign an object class label to each pixel in the DSMs. For the label assignment task, we select the roof type classification problem to distinguish between flat, non-flat, and background pixels. To realize those tasks, we train a conditional generative adversarial network (cGAN) with an objective function based on least squares residuals and an auxiliary term based on normal vectors for further roof surface refinement. Besides, we investigate recently published deep learning architectures for both tasks and develop the final end-to-end network, which combines different models, as using them first separately, they provide the best results for their individual tasks.
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Takaku, J., T. Tadono, M. Doutsu, F. Ohgushi, and H. Kai. "UPDATES OF ‘AW3D30’ ALOS GLOBAL DIGITAL SURFACE MODEL WITH OTHER OPEN ACCESS DATASETS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 24, 2020): 183–89. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-183-2020.
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Abstract. In 2016 we first completed the global data processing of digital surface models (DSMs) by using the whole archives of stereo imageries derived from the Panchromatic Remote sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS). The dataset was freely released to the public in 30 m grid spacing as the ‘ALOS World 3D - 30m (AW3D30)’, which was generated from its original version processed in 5 m or 2.5 m grid spacing. The dataset has been updated since then to improve the absolute/relative height accuracies with additional calibrations. However the most significant update that should be applied for improving the data usability is the filling of void areas, which correspond to approx. 10% of global coverage, mostly due to cloud covers. In this paper we introduce the updates of AW3D30 filling the voids with other open-access DSMs such as Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM), ArcticDEM, etc., through inter-comparisons among these datasets.
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Ganci, Gaetana, Annalisa Cappello, and Marco Neri. "Data Fusion for Satellite-Derived Earth Surface: The 2021 Topographic Map of Etna Volcano." Remote Sensing 15, no. 1 (December 30, 2022): 198. http://dx.doi.org/10.3390/rs15010198.
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We present a new automatic procedure for updating digital topographic data from multi-source satellite imagery, which consists in the production of Digital Surface Models (DSMs) from high resolution optical satellite images, followed by a context-aware fusion that exploits the complementary characteristics of the multi-source DSMs. The fused DSM minimizes blunders and artifacts due to occlusions (e.g., the presence of clouds, snow or ash plumes) in the source images, resulting in improved accuracy and quality versus those that are not merged. The procedure has been tested to produce the 2021 digital topography of Mt Etna, whose summit area is constantly changing and shows the new peak of 3347 m on the north rim of the South East Crater. We also employ the 2021 DSM to measure the volcanic deposits emplaced in the last five years, finding about 120 million cubic meters, with a yearly average volume of about 24 million cubic meters in agreement with the large eruptive rates registered at Mt Etna since the nineteen seventies. The flexibility and modularity of the presented procedure make it easily exportable to other environmental contexts, allowing for a fast and frequent reconstruction of topographic surfaces even in extreme environments.
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Xu, Ningli, and Rongjun Qin. "Large-scale DSM registration via motion averaging." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1-2024 (May 9, 2024): 275–82. http://dx.doi.org/10.5194/isprs-annals-x-1-2024-275-2024.
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Abstract. Generating wide-area digital surface models (DSMs) requires registering a large number of individual, and partially overlapped DSMs. This presents a challenging problem for a typical registration algorithm, since when a large number of observations from these multiple DSMs are considered, it may easily cause memory overflow. Sequential registration algorithms, although can significantly reduce the computation, are especially vulnerable for small overlapped pairs, leading to a large error accumulation. In this work, we propose a novel solution that builds the DSM registration task as a motion averaging problem: pair-wise DSMs are registered to build a scene graph, with edges representing relative poses between DSMs. Specifically, based on the grid structure of the large DSM, the pair-wise registration is performed using a novel nearest neighbor search method. We show that the scene graph can be optimized via an extremely fast motion average algorithm with O(N) complexity (N refers to the number of images). Evaluation of high-resolution satellite-derived DSM demonstrates significant improvement in computation and accuracy.
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Misra, Prakhar, Ram Avtar, and Wataru Takeuchi. "Comparison of Digital Building Height Models Extracted from AW3D, TanDEM-X, ASTER, and SRTM Digital Surface Models over Yangon City." Remote Sensing 10, no. 12 (December 11, 2018): 2008. http://dx.doi.org/10.3390/rs10122008.
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Vertical urban growth in the form of urban volume or building height is increasingly being seen as a significant indicator and constituent of the urban environment. Although high-resolution digital surface models can provide valuable information, various places lack access to such resources. The objective of this study is to explore the feasibility of using open digital surface models (DSMs), such as the AW3D30, ASTER, and SRTM datasets, for extracting digital building height models (DBHs) and comparing their accuracy. A multidirectional processing and slope-dependent filtering approach for DBH extraction was used. Yangon was chosen as the study location since it represents a rapidly developing Asian city where urban changes can be observed during the acquisition period of the aforementioned open DSM datasets (2001–2011). The effect of resolution degradation on the accuracy of the coarse AW3D30 DBH with respect to the high-resolution AW3D5 DBH was also examined. It is concluded that AW3D30 is the most suitable open DSM for DBH generation and for observing buildings taller than 9 m. Furthermore, the AW3D30 DBH, ASTER DBH, and SRTM DBH are suitable for observing vertical changes in urban structures.
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Admassu, Yonathan. "Digital Surface Model-aided Quantitative Geologic Rockfall Rating System (QG-RRS)." Environmental and Engineering Geoscience 25, no. 4 (November 8, 2019): 255–71. http://dx.doi.org/10.2113/eeg-2102.
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ABSTRACT Rockfalls are one of the most common types of slope failures that affect cut slopes along roadways in mountainous regions. The Rockfall Hazard Rating System (RHRS), started in Oregon and adopted by various U.S. states, is used to rate cut slopes with respect to their likelihood of releasing rockfalls. Existing rating systems use semi-quantitative approaches to rate geological and non-geological factors. The main geologic factors are favorability/unfavorability of orientation of discontinuities with respect to the orientation of slope faces and likelihood of differential weathering leading to undercutting of strong rock units. Digital surface models (DSMs) derived from light detection and ranging (LiDAR) and photogrammetry have been used to remotely characterize rock mass. This research introduces an expanded application of DSMs to quantify geologic factors that contribute to the likelihood of rockfall events. The method is hence referred to as the Quantitative Geologic Rockfall Rating System (QG-RRS). Four DSM-based parameters, A, B, C, and D, have been identified to evaluate geologic factors. These parameters quantify the likelihood of discontinuity orientation-controlled failures (parameter A), the degree of undercutting (parameter B), rockfall activity based on rockfall release surfaces (parameter C), and rockfall volume from rockfall voids (parameter D). This rating system, although not inclusive of other non-geological factors, appears to provide reproducible quantitative estimation of geologic factors that control rockfall generation.
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Skarlatos, D., and M. Vlachos. "VEGETATION REMOVAL FROM UAV DERIVED DSMS, USING COMBINATION OF RGB AND NIR IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2 (May 28, 2018): 255–62. http://dx.doi.org/10.5194/isprs-annals-iv-2-255-2018.
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Current advancements on photogrammetric software along with affordability and wide spreading of Unmanned Aerial Vehicles (UAV), allow for rapid, timely and accurate 3D modelling and mapping of small to medium sized areas. Although the importance and applications of large format aerial overlaps cameras and photographs in Digital Surface Model (DSM) production and LIDAR data is well documented in literature, this is not the case for UAV photography. Additionally, the main disadvantage of photogrammetry is the inability to map the dead ground (terrain), when we deal with areas that include vegetation. This paper assesses the use of near-infrared imagery captured by small UAV platforms to automatically remove vegetation from Digital Surface Models (DSMs) and obtain a Digital Terrain Model (DTM). Two areas were tested, based on the availability of ground reference points, both under trees and among vegetation, as well as on terrain. In addition, RGB and near-infrared UAV photography was captured and processed using Structure from Motion (SfM) and Multi View Stereo (MVS) algorithms to generate DSMs and corresponding colour and NIR orthoimages with 0.2&thinsp;m and 0.25&thinsp;m as pixel size respectively for the two test sites. Moreover, orthophotos were used to eliminate the vegetation from the DSMs using NDVI index, thresholding and masking. Following that, different interpolation algorithms, according to the test sites, were applied to fill in the gaps and created DTMs. Finally, a statistic analysis was made using reference terrain points captured on field, both on dead ground and under vegetation to evaluate the accuracy of the whole process and assess the overall accuracy of the derived DTMs in contrast with the DSMs.
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Lagner, Ondřej, Tomáš Klouček, and Petra Šímová. "Impact of input data (in)accuracy on overestimation of visible area in digital viewshed models." PeerJ 6 (May 21, 2018): e4835. http://dx.doi.org/10.7717/peerj.4835.
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Viewshed analysis is a GIS tool in standard use for more than two decades to perform numerous scientific and practical tasks. The reliability of the resulting viewshed model depends on the computational algorithm and the quality of the input digital surface model (DSM). Although many studies have dealt with improving viewshed algorithms, only a few studies have focused on the effect of the spatial accuracy of input data. Here, we compare simple binary viewshed models based on DSMs having varying levels of detail with viewshed models created using LiDAR DSM. The compared DSMs were calculated as the sums of digital terrain models (DTMs) and layers of forests and buildings with expertly assigned heights. Both elevation data and the visibility obstacle layers were prepared using digital vector maps differing in scale (1:5,000, 1:25,000, and 1:500,000) as well as using a combination of a LiDAR DTM with objects vectorized on an orthophotomap. All analyses were performed for 104 sample locations of 5 km2, covering areas from lowlands to mountains and including farmlands as well as afforested landscapes. We worked with two observer point heights, the first (1.8 m) simulating observation by a person standing on the ground and the second (80 m) as observation from high structures such as wind turbines, and with five estimates of forest heights (15, 20, 25, 30, and 35 m). At all height estimations, all of the vector-based DSMs used resulted in overestimations of visible areas considerably greater than those from the LiDAR DSM. In comparison to the effect from input data scale, the effect from object height estimation was shown to be secondary.
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Dhanda, A., G. Scarpa, S. Fai, and M. Santana Quintero. "A WORKFLOW FOR GEOMETRIC COLOUR PHOTOGRAPHY OF PAINTED SURFACES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W11 (May 4, 2019): 469–74. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w11-469-2019.
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<p><strong>Abstract.</strong> Colour fidelity is vital when documenting painted surfaces. The 2.5D nature of many painted surfaces makes orthophotos and digital surface models (DSMs) common products of the documentation process. This paper presents a workflow to combine photographic and photogrammetric methods to produce aligned colour and depth (orthophotos and DSMs). First, two photogrammetric software (Agisoft Photoscan and Capturing Reality Reality Capture) were tested to determine if they adjusted the colour data during the processing stages. It was found that Photoscan can produce 16-bit orthophotos without manipulating the data; however, Reality Capture is currently limited to 8-bit results. When capturing a surface using photogrammetry, it is common to use the same data for colour and depth. The presented workflow, however, argues that better colour accuracy can be achieved by capturing the two datasets separately and combining them in photogrammetric software. The workflow is demonstrated through the documentation of an unnamed religious painting from the 17th century.</p>
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Moya, Luis, Fumio Yamazaki, Wen Liu, and Tatsuro Chiba. "Calculation of coseismic displacement from lidar data in the 2016 Kumamoto, Japan, earthquake." Natural Hazards and Earth System Sciences 17, no. 1 (February 1, 2017): 143–56. http://dx.doi.org/10.5194/nhess-17-143-2017.
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Abstract. The spatial distribution of the coseismic displacements that occurred along the Futagawa fault during the 2016 Kumamoto earthquake of Mw 7.0 was estimated using airborne light detection and ranging (lidar) data. In this study, a pair of digital surface models (DSMs) obtained from the high-density lidar data before and after the mainshock on 16 April 2016 were used. A window matching search approach based on the correlation coefficient between the two DSMs was used to estimate the geodetic displacement in the near-field region. The results showed good agreements with the geodetic displacements calculated from strong-motion acceleration records and coincided with the fault line surveyed by the Geological Survey of Japan.
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Izumida, Atsuto, Shoichiro Uchiyama, and Toshihiko Sugai. "Application of UAV-SfM photogrammetry and aerial lidar to a disastrous flood: repeated topographic measurement of a newly formed crevasse splay of the Kinu River, central Japan." Natural Hazards and Earth System Sciences 17, no. 9 (September 13, 2017): 1505–19. http://dx.doi.org/10.5194/nhess-17-1505-2017.
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Abstract. Geomorphic impacts of a disastrous crevasse splay that formed in September 2015 and its post-formation modifications were quantitatively documented by using repeated, high-definition digital surface models (DSMs) of an inhabited and cultivated floodplain of the Kinu River, central Japan. The DSMs were based on pre-flood (resolution: 2 m) and post-flood (resolution: 1 m) aerial light detection and ranging (lidar) data from January 2007 and September 2015, respectively, and on structure-from-motion (SfM) photogrammetry data (resolution: 3.84 cm) derived from aerial photos taken by an unmanned aerial vehicle (UAV) in December 2015. After elimination of systematic errors among the DSMs and down-sampling of the SfM-derived DSM, elevation changes on the order of 10−1 m – including not only topography but also growth of vegetation, vanishing of flood waters, and restoration and repair works – were detected. Comparison of the DSMs showed that the volume eroded by the flood was more than twice the deposited volume in the area within 300–500 m of the breached artificial levee, where the topography was significantly affected. The results suggest that DSMs based on a combination of UAV-SfM and lidar data can be used to quantify, rapidly and in rich detail, topographic changes on floodplains caused by floods.
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Waser, L. T., E. Baltsavias, K. Ecker, H. Eisenbeiss, C. Ginzler, M. Küchler, P. Thee, and L. Zhang. "High‐resolution digital surface models (DSMs) for modelling fractional shrub/tree cover in a mire environment." International Journal of Remote Sensing 29, no. 5 (February 25, 2008): 1261–76. http://dx.doi.org/10.1080/01431160701736422.
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Sefercik, U. G., and U. Soergel. "Influence of temporal baseline on the vertical absolute accuracies of TSX HS interferometric DSMs: Case Study on Berlin." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 155–60. http://dx.doi.org/10.5194/isprsarchives-xl-7-155-2014.
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In recent years, interferometric sytnthetic aperture radar (InSAR) is one of the most preferred techniques to generate digital surface models (DSM) which are the three dimensional (3D) digital cartographic representations of earth surface including all terrain and non-terrain formations. Interferometric DSM generation using synthetic aperture radar (SAR) imagery is not an easy process and the vertical absolute accuracy of the final product depends on various parameters. In this study, we aimed to demonstrate the influence of temporal baseline between SAR image-pairs on the vertical absolute accuracy of high resolution interferometric DSMs. The application was realized covering 20 km<sup>2</sup> area in Berlin, Germany using 15 descending orbit high resolution spotlight (HS) TerraSAR-X (TSX) images. The suitable interferometric pairs were determined for DSM generation and two of them that have similar parameters except temporal baseline were selected regarding the purposes of the study. The master image was selected as same in the generation of both DSMs and the temporal baselines between this master image and slave images were 11 days (1 period) and 187 days (17 periods), respectively. TSX HS DSMs were generated with 2 m grid spacing and the vertical absolute accuracies were calculated based on the comparison with a reference DSM generated by radargrammetry. The analyses were realized for built-up and forest land classes separately. The results proved that longer temporal baseline has negative influence on the vertical absolute accuracies of TSX HS interferometric DSMs. The first DSM which has the shortest temporal baseline, possible for TSX sensing is better than the second one as approx. 1.5 m both for built-up and forest areas.
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de Sousa, Amanda Mendes, Camila Duelis Viana, Guilherme Pereira Bento Garcia, and Carlos Henrique Grohmann. "Monitoring Geological Risk Areas in the City of São Paulo Based on Multi-Temporal High-Resolution 3D Models." Remote Sensing 15, no. 12 (June 9, 2023): 3028. http://dx.doi.org/10.3390/rs15123028.
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This paper presents a multi-temporal comparison of high-resolution 3D digital models from two urban areas susceptible to landslides in three time periods. The study areas belong to the São Paulo landslide risk mapping database and are named “CEU Paz” (CP) and “Parque Santa Madalena I” (PSM). For each area, a lidar digital surface model (DSM) (2017) and two structure-from-motion multi-view stereo DSMs (2019 and 2022) built from drone imagery were combined using raster algebra to generate three digital surface models of differences (DoDs). The DoDs were able to highlight changes in vegetation cover and buildings, which are important characteristics for evaluating geological risks in an urban context. Still, they were unable to highlight changes in the ground surface. The results demonstrate that the method greatly supports monitoring, allowing for greater detail and ease of detecting large-scale changes. Even with promising results, this technique should be understood as one more tool for mapping risk areas without replacing fieldwork.
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Wan, Qifeng, Yuzheng Guan, Qiang Zhao, Xiang Wen, and Jiangfeng She. "Constraining the Geometry of NeRFs for Accurate DSM Generation from Multi-View Satellite Images." ISPRS International Journal of Geo-Information 13, no. 7 (July 8, 2024): 243. http://dx.doi.org/10.3390/ijgi13070243.
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Neural Radiance Fields (NeRFs) are an emerging approach to 3D reconstruction that use neural networks to reconstruct scenes. However, its applications for multi-view satellite photogrammetry, which aim to reconstruct the Earth’s surface, struggle to acquire accurate digital surface models (DSMs). To address this issue, a novel framework, Geometric Constrained Neural Radiance Field (GC-NeRF) tailored for multi-view satellite photogrammetry, is proposed. GC-NeRF achieves higher DSM accuracy from multi-view satellite images. The key point of this approach is a geometric loss term, which constrains the scene geometry by making the scene surface thinner. The geometric loss term alongside z-axis scene stretching and multi-view DSM fusion strategies greatly improve the accuracy of generated DSMs. During training, bundle-adjustment-refined satellite camera models are used to cast rays through the scene. To avoid the additional input of altitude bounds described in previous works, the sparse point cloud resulting from the bundle adjustment is converted to an occupancy grid to guide the ray sampling. Experiments on WorldView-3 images indicate GC-NeRF’s superiority in accurate DSM generation from multi-view satellite images.
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Liang, Jianming, Jianhua Gong, Xiuping Xie, and Jun Sun. "Solar3D: An Open-Source Tool for Estimating Solar Radiation in Urban Environments." ISPRS International Journal of Geo-Information 9, no. 9 (September 1, 2020): 524. http://dx.doi.org/10.3390/ijgi9090524.
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Solar3D is an open-source software application designed to interactively calculate solar irradiation on three-dimensional (3D) surfaces in a virtual environment constructed with combinations of 3D-city models, digital elevation models (DEMs), digital surface models (DSMs) and feature layers. The GRASS GIS r.sun solar radiation model computes solar irradiation based on two-dimensional (2D) raster maps for a given day, latitude, surface and atmospheric conditions. With the increasing availability of 3D-city models and demand for solar energy, there is an urgent need for better tools to computes solar radiation directly with 3D-city models. Solar3D extends the GRASS GIS r.sun model from 2D to 3D by feeding the model with input, including surface slope, aspect and time-resolved shading, which is derived directly from the 3D scene using computer graphics techniques. To summarize, Solar3D offers several new features that—as a whole—distinguish this novel approach from existing 3D solar irradiation tools in the following ways. (1) Solar3D can consume massive heterogeneous 3D-city models, including massive 3D-city models such as oblique airborne photogrammetry-based 3D-city models (OAP3Ds or integrated meshes); (2) Solar3D can perform near real-time pointwise calculation for duration from daily to annual; (3) Solar3D can integrate and interactively explore large-scale heterogeneous geospatial data; (4) Solar3D can calculate solar irradiation at arbitrary surface positions including on rooftops, facades and the ground.
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Yamazaki, Fumio, Wen Liu, and Kei Horie. "Use of Multi-Temporal LiDAR Data to Extract Collapsed Buildings and to Monitor Their Removal Process after the 2016 Kumamoto Earthquake." Remote Sensing 14, no. 23 (November 25, 2022): 5970. http://dx.doi.org/10.3390/rs14235970.
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This study demonstrates the use of multi-temporal LiDAR data to extract collapsed buildings and to monitor their removal process in Minami-Aso village, Kumamoto prefecture, Japan, after the April 2016 Kumamoto earthquake. By taking the difference in digital surface models (DSMs) acquired at pre- and post-event times, collapsed buildings were extracted and the results were compared with damage survey data by the municipal government and aerial optical images. Approximately 40% of severely damaged buildings showed a reduction in the average height within a reduced building footprint between the pre- and post-event DSMs. Comparing the removal process of buildings in the post-event periods with the damage classification result from the municipal government, the damage level was found to affect judgements by the owners regarding demolition and removal.
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Khakimova, Kamola, Dilobar Mamanazarova, Nilufar Urinova, Bakhtigul Mamadaminova, and Shoira Jumayeva. "Design of an optimal irrigation system taking into account the characteristics of the earth using simulation-optimization models." E3S Web of Conferences 590 (2024): 02008. http://dx.doi.org/10.1051/e3sconf/202459002008.
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This paper presents a novel algorithm for the design of an optimal irrigation system, which utilizes land topographic data, specifically digital land surface models (DSMs). The proposed approach incorporates an inter-farm irrigation network map, canal profile, and spatial model of the region as part of the topographic data. By dividing the land into sections with similar topographical characteristics, this method facilitates the identification and optimization of key parameters related to the land and irrigation system, ultimately leading to an optimal design of the irrigation system.
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Bittner, K., L. Liebel, M. Körner, and P. Reinartz. "LONG-SHORT SKIP CONNECTIONS IN DEEP NEURAL NETWORKS FOR DSM REFINEMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 12, 2020): 383–90. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-383-2020.
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Abstract. Detailed digital surface models (DSMs) from space-borne sensors are the key to successful solutions for many remote sensing problems, like environmental disaster simulations, change detection in rural and urban areas, 3D urban modeling for city planning and management, etc. Traditional methodologies, e.g., stereo matching, used to generate photogrammetric DSMs from stereo imagery, usually deliver low-quality results due to the matching errors in homogeneous areas or the lack of information when observing the scene under different viewing angles. This makes the tasks related to building reconstruction very challenging since in most cases it is difficult to recognize the type of roofs, especially if overlaid with trees. This work represents a continuation of research regarding the automatic optimization of building geometries in photogrammetric DSMs with half-meter resolution and introduces an improved generative adversarial network (GAN) architecture which allows to reconstruct complete and detailed building structures without neglecting even low-rise urban constructions. The generative part of the network is constructed in a way that it simultaneously processes height and intensity information, and combines short and long skip connections within one architecture. To improve different aspects of the surface, several loss terms are used, the contributions of which are automatically balanced during training. The obtained results demonstrate that the proposed methodology can achieve two goals without any manual intervention: improve the roof surfaces by making them more planar and also recognize and optimize even small residential buildings which are hard to detect.
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Wang, Jinghui, Ke Gong, Timo Balz, Norbert Haala, Uwe Soergel, Lu Zhang, and Mingsheng Liao. "Radargrammetric DSM Generation by Semi-Global Matching and Evaluation of Penalty Functions." Remote Sensing 14, no. 8 (April 7, 2022): 1778. http://dx.doi.org/10.3390/rs14081778.
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Radargrammetry is a useful approach to generate Digital Surface Models (DSMs) and an alternative to InSAR techniques that are subject to temporal or atmospheric decorrelation. Stereo image matching in radargrammetry refers to the process of determining homologous points in two images. The performance of image matching influences the final quality of DSM used for spatial-temporal analysis of landscapes and terrain. In SAR image matching, local matching methods are commonly used but usually produce sparse and inaccurate homologous points adding ambiguity to final products; global or semi-global matching methods are seldom applied even though more accurate and dense homologous points can be yielded. To fill this gap, we propose a hierarchical semi-global matching (SGM) pipeline to reconstruct DSMs in forested and mountainous regions using stereo TerraSAR-X images. In addition, three penalty functions were implemented in the pipeline and evaluated for effectiveness. To make accuracy and efficiency comparisons between our SGM dense matching method and the local matching method, the normalized cross-correlation (NCC) local matching method was also applied to generate DSMs using the same test data. The accuracy of radargrammetric DSMs was validated against an airborne photogrammetric reference DSM and compared with the accuracy of NASA’s 30 m SRTM DEM. The results show the SGM pipeline produces DSMs with height accuracy and computing efficiency that exceeds the SRTM DEM and NCC-derived DSMs. The penalty function adopting the Canny edge detector yields a higher vertical precision than the other two evaluated penalty functions. SGM is a powerful and efficient tool to produce high-quality DSMs using stereo Spaceborne SAR images.
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d’Angelo, P., and F. Kurz. "AIRCRAFT BASED REAL TIME BUNDLE ADJUSTMENT AND DIGITAL SURFACE MODEL GENERATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1643–47. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1643-2019.
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<p><strong>Abstract.</strong> This paper introduces a system for real-time generation of digital surface models (DSM) based on an optical multi-camera system flown on board of a manned airplane or helicopter. The system consists of high end consumer cameras, GNSS/IMU system, and on-board computers for real-time data processing. Usually, generation of digital surface models from aerial imagery is done in an off-line process, leading to delayed availability of height data. The proposed system processes data in real time on board of the aircraft and downlinks the generated DSM to a ground station. This paper evaluates the GNSS/IMU on-line solution quality and its impact on dense stereo matching. The proposed real time sliding window based bundle adjustment significantly improves image orientations and DSM quality, allowing generation of detailed digital surface models with a resolution of 2*GSD. Experiments using two flight patterns are conducted over the city of Landsberg and the resulting DSMs are evaluated against a LiDAR generated reference point cloud. The online bundle adjustment is shown to minimize the effect of systematic GNSS/IMU offsets while adding only a limited delay.</p>