Gotowa bibliografia na temat „Over-constrained temporal problems”

Over-subscription, that is, being assigned too many things to do, is commonly encountered in temporal scheduling problems. As human beings, we often want to do more than we can actually do, and underestimate how long it takes to perform each task. Decision makers can benefit from aids that identify when these failure situations are likely, the root causes of these failures, and resolutions to these failures. In this paper, we present a decision assistant that helps users resolve over-subscribed temporal problems. The system works like an experienced advisor that can quickly identify the cause of failure underlying temporal problems and compute resolutions. The core of the decision assistant is the Best-first Conflict-Directed Relaxation (BCDR) algorithm, which can detect conflicting sets of constraints within temporal problems, and computes continuous relaxations for them that weaken constraints to the minimum extent, instead of removing them completely. BCDR is an extension to the Conflict-Directed A* algorithm, first developed in the model-based reasoning community to compute most likely system diagnoses or reconfigurations. It generalizes the discrete conflicts and relaxations, to hybrid conflicts and relaxations, which denote minimal inconsistencies and minimal relaxations to both discrete and continuous relaxable constraints. In addition, BCDR is capable of handling temporal uncertainty, expressed as either set-bounded or probabilistic durations, and can compute preferred trade-offs between the risk of violating a schedule requirement, versus the loss of utility by weakening those requirements. BCDR has been applied to several decision support applications in different domains, including deep-sea exploration, urban travel planning and transit system management. It has demonstrated its effectiveness in helping users resolve over-subscribed scheduling problems and evaluate the robustness of existing solutions. In our benchmark experiments, BCDR has also demonstrated its efficiency on solving large-scale scheduling problems in the aforementioned domains. Thanks to its conflict-driven approach for computing relaxations, BCDR achieves one to two orders of magnitude improvements on runtime performance when compared to state-of-the-art numerical solvers.

Uncertainty is commonly encountered in temporal scheduling and planning problems, and can often lead to over-constrained situations. Previous relaxation algorithms for over-constrained temporal problems only work with requirement constraints, whose outcomes can be controlled by the agents. When applied to uncontrollable durations, these algorithms may only satisfy a subset of the random outcomes and hence their relaxations may fail during execution. In this paper, we present a new relaxation algorithm, Conflict-Directed Relaxation with Uncertainty (CDRU), which generates relaxations that restore the controllability of conditional temporal problems with uncontrollable durations. CDRU extends the Best-first Conflict-Directed Relaxation (BCDR) algorithm to uncontrollable temporal problems. It generalizes the conflict-learning process to extract conflicts from strong and dynamic controllability checking algorithms, and resolves the conflicts by both relaxing constraints and tightening uncontrollable durations. Empirical test results on a range of trip scheduling problems show that CDRU is efficient in resolving large scale uncontrollable problems: computing strongly controllable relaxations takes the same order of magnitude in time compared to consistent relaxations that do not account for uncontrollable durations. While computing dynamically controllable relaxations takes two orders of magnitude more time, it provides significant improvements in solution quality when compared to strongly controllable relaxations.

Solving planning and scheduling problems for multiple tasks with highly coupled state and temporal constraints is notoriously challenging. An appealing approach to effectively decouple the problem is to judiciously order the events such that decisions can be made over sequences of tasks. As many problems encountered in practice are over-constrained, we must instead find relaxed solutions in which certain requirements are dropped. This motivates a formulation of optimality with respect to the costs of relaxing constraints and the problem of finding an optimal ordering under which this relaxing cost is minimum. In this paper, we present Generalized Conflict-directed Ordering (GCDO), a branch-and-bound ordering method that generates an optimal total order of events by leveraging the generalized conflicts of both inconsistency and suboptimality from sub-solvers for cost estimation and solution space pruning. Due to its ability to reason over generalized conflicts, GCDO is much more efficient in finding high-quality total orders than the previous conflict-directed approach CDITO. We demonstrate this by benchmarking on temporal network configuration problems, which involves managing networks over time and makes necessary tradeoffs between network flows against CDITO and Mixed Integer-Linear Programing (MILP). Our algorithm is able to solve two orders of magnitude more benchmark problems to optimality and twice the problems compared to CDITO and MILP within a runtime limit, respectively.

In this paper we present an approximation method based on discrete Hopfield neural network (DHNN) for solving temporal constraint satisfaction problems. This method is of interest for problems involving numeric and symbolic temporal constraints and where a solution satisfying the constraints of the problem needs to be found within a given deadline. More precisely the method has the ability to provide a solution with a quality proportional to the allocated process time. The quality of the solution corresponds here to the number of satisfied constraints. This property is very important for real world applications including reactive scheduling and planning and also for over constrained problems where a complete solution cannot be found. Experimental study, in terms of time cost and quality of the solution provided, of the DHNN based method we propose provides promising results comparing to the other exact methods based on branch and bound and approximation methods based on stochastic local search.

Time management under uncertainty is essential to large scale projects. From space exploration to industrial production, there is a need to schedule and perform activities. given complex specifications on timing. In order to generate schedules that are robust to uncertainty in the duration of activities, prior work has focused on a problem framing that uses an interval-bounded uncertainty representation. However, such approaches are unable to take advantage of known probability distributions over duration. In this paper we concentrate on a probabilistic formulation of temporal problems with uncertain duration, called the probabilistic simple temporal problem. As distributions often have an unbounded range of outcomes, we consider chance-constrained solutions, with guarantees on the probability of meeting temporal constraints. By considering distributions over uncertain duration, we are able to use risk as a resource, reason over the relative likelihood of outcomes, and derive higher utility solutions. We first demonstrate our approach by encoding the problem as a convex program. We then develop a more efficient hybrid algorithm whose parent solver generates risk allocations and whose child solver generates schedules for a particular risk allocation. The child is made efficient by leveraging existing interval-bounded scheduling algorithms, while the parent is made efficient by extracting conflicts over risk allocations. We perform numerical experiments to show the advantages of reasoning over probabilistic uncertainty, by comparing the utility of schedules generated with risk allocation against those generated from reasoning over bounded uncertainty. We also empirically show that solution time is greatly reduced by incorporating conflict-directed risk allocation.

Abstract We propose a new approach to solve inter-temporal unit aggregation issues under maximum opening size requirements using two models. The first model is based on Model I formulation with static harvest treatments for harvest activities. This model identifies periodic harvest activities using a set of constraints for inter-temporal aggregation. The second model is based on Model II formulation, which uses dynamic harvest treatments and incorporates periodic harvest activities directly into the model formulation. The proposed approach contributes to the literature on spatially constrained harvest scheduling problems as it allows a pattern of unit aggregation to change across multiple harvests over time, as inter-temporal aggregation under a maximum opening size requirement over period-specific duration. The main idea of the proposed approach for inter-temporal aggregation is to use a multiple layer scheme for a set of spatial constraints, which is adapted from a maximum flow specification in a spatial forest unit network and a sequential triangle connection to create fully connected feasible clusters. By dividing the planning horizon into period-specific durations for different spatial aggregation patterns, the models can complete inter-temporal spatial aggregation over the planning horizon under a maximum opening size requirement per duration.

Spatial management issues have assumed a central position in planning for forest ecosystems in the United States on both public and private lands. The arrangement of management activities, especially harvesting activities, can often have adverse impacts on other neighboring areas of the forest. Thus, spatially explicit programming models, which can account for or prevent certain arrangements of activities or land allocations through the use of harvest adjacency constraints, have received considerable attention in the literature. The need for spatial specificity in programming models has led to the development of integer programming or mixed integer programming models. Given that integer programming problems are often viewed as a difficult class of problems to solve, heuristic solution methods have most often been used to solve spatially constrained forest management models. In this paper, a discrete (0–1) integer programming model that maximizes harvested timber volume over a multiperiod time horizon subject to harvest adjacency constraints is developed and tested for irregular, realistic systems of parcels. This model performed well computationally for many example configurations and was solved exactly using the simplex algorithm and limited branching and bounding. Certain spatial configurations with long time horizons did, however, require a nontrivial amount of branching and bounding. The model was tested using both contrived and real spatial data sets.

Despite some success of Genetic Algorithms (GAs) when tackling Constraint Satisfaction Problems (CSPs), they generally suffer from poor crossover operators. In order to overcome this limitation in practice, we propose a novel crossover specifically designed for solving CSPs including Temporal CSPs (TCSPs). Together with a variable ordering heuristic and an integration into a parallel architecture, this proposed crossover enables the solving of large and hard problem instances as demonstrated by the experimental tests conducted on randomly generated CSPs and TCSPs based on the model RB. We will indeed demonstrate, through these tests, that our proposed method is superior to the known GA-based techniques for CSPs. In addition, we will show that we are able to compete with the efficient MAC-based Abscon 109 solver for random problem instances as well as those instances taken from Lecoutre’s CSP library. Finally, we conducted additional tests on very large consistent and over constrained CSPs and TCSPs instances in order to show the ability of our method to deal with constraint problems in real time. This corresponds to solving the CSP or the TCSP by giving a solution with a quality (number of solved constraints) depending on the time allocated for computation.

Aquaculture plays an important role in China’s total fisheries production nowadays, and it leads to a few problems, for example water quality degradation, which has damaging effect on the sustainable development of environment. Among the many forms of aquaculture that deteriorate the water quality, disorderly pen culture is especially severe. Pen culture began very early in Yangchenghu Lake and Taihu Lake in China and part of the pen culture still exists. Thus, it is of great significance to evaluate the distribution and area of the pen culture in the two lakes. However, the traditional method for pen culture detection is based on the factual measurement, which is labor and time consuming. At present, with the development of remote sensing technologies, some target detection algorithms for multi/hyper-spectral data have been used in the pen culture detection, but most of them are intended for the single-temporal remote sensing data. Recently, a target detection algorithm called filter tensor analysis (FTA), which is specially designed for multi-temporal remote sensing data, has been reported and has achieved better detection results compared to the traditional single-temporal methods in many cases. This paper mainly aims to investigate the pen culture in Yangchenghu Lake and Taihu Lake with FTA implemented on the multi-temporal Landsat imagery, by determining the optimal time phases combination of the Landsat data in advance. Furthermore, the suitability and superiority of FTA over Constrained Energy Minimization (CEM) in the process of pen culture detection were tested. It was observed in the experiments on the data of those two lakes that FTA can detect the pen culture much more accurately than CEM with Landsat data of selected bands and of limited number of time phases.

Temporal reasoning has been an active research area for over twenty years, with most work focussing on either enhancing the efficiency of current temporal reasoning algorithms or enriching the existing algebras. However, there has been little research into handling over-constrained temporal problems except to recognise that a problem is over-constrained and then to terminate. As many real-world temporal reasoning problems are inherently over-constrained, particularly in the scheduling domain, there is a significant need for approaches that can handle over-constrained situations. In this thesis, we propose two backtracking algorithms to gain partial solutions to over-constrained temporal problems. We also propose a new representation, the end-point ordering model, to allow the use of local search algorithms for temporal reasoning. Using this model we propose a constraint weighting local search algorithm as well as tabu and random-restart algorithms to gain partial solutions to over-constrained temporal problems. Specifically, the contributions of this thesis are: The introduction and empirical evaluation of two backtracking algorithms to solve over-constrained temporal problems. We provide two backtracking algorithms to close the gap in current temporal research to solve over-constrained problems; The representation of temporal constraint networks using the end-point ordering model. As current representation models are not suited for local search algorithms, we develop a new model such that local search can be applied efficiently to temporal reasoning; The development of a constraint weighting local search algorithm for under-constrained problems. As constraint weighting has proven to be efficient for solving many CSP problems, we implement a constraint weighting algorithm to solve under-constrained temporal problems; An empirical evaluation of constraint weighting local search against traditional backtracking algorithms. We compare the results of a constraint weighting algorithm with traditional backtracking approaches and find that in many cases constraint weighting has superior performance; The development of a constraint weighting local search, tabu search and random-restart local search algorithm for over-constrained temporal problems. We extend our constraint weighting algorithm to solve under-constrained temporal problems as well as implement two other popular local search algorithms: tabu search and random-restart; An empirical evaluation of all three local search algorithms against the two backtracking algorithms. We compare the results of all three local search algorithms with our twobacktracking algorithms for solving over-constrained temporal reasoning problems and find that local search proves to be considerably superior.

The paper introduces a new, intrinsically discrete, path planning and collision-avoidance problem, with multiple robots and multiple goals. The issue arises in the operation of the novel Swing and Dock (SaD) locomotion for a material handling system. Its agents traverse a base grid by sequences of rotations (swings) around fixed pins. Each agent must visit an array of goal positions in minimal time while avoiding collisions. The corresponding off-line path-planning problem is NP-hard. We model the system by an extended temporal graph and introduce two integer linear programming (ILP) formulations for the minimization of the makespan, with decision variables on the nodes and the edges, respectively. Both optimizations are constrained and favor idling over detours to reduce mechanical wear. The ILP formulations, tailored to the SaD system, are general enough to be applicable for many other single- and multi-agent problems over discretized networks. We have implemented the ILPs with a gurobi solver. Computational results demonstrate and compare the effectiveness of the two formulations.

One can recover the motion of an observer in a static environment directly from first derivatives (spatial and temporal) of image brightness, accumulated over a large part of the image. There is no need to extract features and to determine correspondences between features in successive frames. This is fortunate, since the correspondence problem in the case of motion vision is even harder than it is for binocular stereo, where matching features have to lie on corresponding epipolar lines. Motion vision—at least short-range motion vision—should be easier than binocular stereo, not harder. In the case of rigid body motion there is also no need to first estimate the optical flow, since it is so highly constrained.

Abstract This paper offers an integrative behavioral-based physics-inspired approach to model and control traffic congestion in an efficient manner While existing physics-based approaches commonly assign density and traffic flow states with the Fundamental Diagram, this paper specifies the flow-density relation using past traffic behavior (intent) recorded over a time sliding window with constant horizon length. With this approach, traffic coordination trends can be consistently learned and incorporated into traffic planning. This is integrated with mass conservation law (continuity) to model traffic coordination as a probabilistic process and obtain traffic feasibility conditions using linear temporal logic. By spatial discretization of a network of inter-connected roads (NOIR), the NOIR is represented by a graph with inlet boundary nodes, outlet boundary nodes, and interior nodes. The paper offers a boundary control approach to manage congestion through the inlet boundary nodes. More specifically, model predictive control (MPC) is applied to control traffic congestion through the boundary of the traffic network. Therefore, the optimal boundary in flow is assigned as the solution of a constrained quadratic programming problem with equality and inequality constrained. The simulation results shows that the proposed MPC boundary controller can successfully control the traffic through the inlet boundary nodes where traffic reaches the steady state condition.

This paper presents an implementation of a novel dynamic formulation on multibody systems involving closed loops. The formulation allows the treatment of time, appearing within the equations of motion, as a variable in much the same manner as having been done by the finite element community on the spatial coordinates. As a consequence, the resulting equations appear together in a single all-encompassing set of algebraic equations which may be solved simultaneously, yielding the system state for all times within the time interval considered. The approach conceivably allows the problem to be parallelized over both space and time, resulting in a far greater level of coarse grain parallelization. As the Stewart Platform is a heavily constrained system with its spatial domain being much smaller than the temporal domain of simulation, it will serve as a perfect example to test the implication of this method. Simulation results obtained from traditional dynamic techniques will be used for verification purposes.

The formal synthesis of automated or autonomous agents has elicited strong interest from the artificial intelligence community in recent years. This problem space broadly entails the derivation of decision-making policies for agents acting in an environment such that a formal specification of behavior is satisfied. Popular formalisms for such specifications include the quintessential Linear Temporal Logic (LTL) and Computation Tree Logic (CTL) which reason over infinite sequences and trees, respectively, of states. However, the related and relevant problem of reasoning over the frequency with which states are visited infinitely and enforcing behavioral specifications on the same has received little attention. That problem, known as Steady-State Policy Synthesis (SSPS) or steady-state control, is the focus of this paper. Prior related work has been mostly confined to unichain Markov Decision Processes (MDPs), while a tractable solution to the general multichain setting heretofore remains elusive. In this paper, we provide a solution to the latter within the context of multichain MDPs over a class of policies that account for all possible transitions in the given MDP. The solution policy is derived from a novel linear program (LP) that encodes constraints on the limiting distributions of the Markov chain induced by said policy. We establish a one-to-one correspondence between the feasible solutions of the LP and the stationary distributions of the induced Markov chains. The derived policy is shown to maximize the reward among the constrained class of stationary policies and to satisfy the specification constraints even when it does not exercise all possible transitions.

The rapid urban development in the Hurghada area since the 1980s has dramatically enhanced the potential impact of human activities. To inventory and monitor this urban development effectively, remote sensing provides a viable source of data from which updated land cover information can be extracted efficiently and cheaply. In this study, data from three satellite datasets, Landsat Thematic Mapper (Landsat 5 TM), Landsat Enhanced Thematic Mapper Plus (Landsat 7 ETM+) and Terra/Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), acquired during 1987, 2000 and 2005, respectively, were used to detect and evaluate Hurghada's urban expansion. Five change detection techniques were tested to detect areas of change. The techniques considered were image differencing, image ratioing, image overlay, multidate principal component analysis (PCA) and post-classification comparison. The post-classification comparison was found to be the most accurate procedure and produced three land use/land cover (LULC) maps of the years 1987, 2000 and 2005 with overall accuracies of 87.8%, 88.9% and 92.0%, respectively. The urban expansion analysis revealed that the built-up area has expanded by 40 km2 in 18 years (1987–2005). In addition, 4.5 km2 of landfill/sedimentation was added to the sea as a result of the coastal urban development and tourist activities. The booming coastal tourism and population pressure were considered to be the main factors driving this expansion, and some natural and artificial constraints constrained the physical shape of the city. The expansion is represented by urban fringe development, linear, infill and isolated models. Topography, lithology and structures were also analysed as possible factors that influenced the expansion. The understanding of the spatial and temporal dynamics of Hurghada's urban expansion is the cornerstone for formulating a view about the future urban uses and for making the best use of the limited resources that are available [1]. A Landsat 5 Thematic Mapper (TM) image of 1987 and a Landsat 7 Enhanced Thematic Mapper Plus (ETM+) image of 2000 were used to examine changes in land use/land cover (LULC) around Hurghada, Egypt, and changes in the composition of coral reefs offshore. Prior to coral reef bottom type classification, the radiance values were transformed to depth invariant bottom indices to reduce the effect of the water column. Subsequently, a multi component change detection procedure was applied to these indices to define changes. Preliminary results showed significant changes in LULC during the period 1987–2000 as well as changes in coral reef composition. Direct impacts along the coastline were clearly shown, but it was more difficult to link offshore changes in coral reef composition to indirect impacts of the changing LULC. Further research is needed to explore the effects of the different image processing steps, and to discover possible links between indirect impacts of LULC changes and changes in the coral reef composition [2]. Knowledge and detecting impacts of human activities on the coastal ecosystem is an essential management requirement and also very important for future and proper planning of coastal areas. Moreover, documentation of these impacts can help in increasing public awareness about side effects of unsustainable practices. Analysis of multidate remote sensing data can be used as an effective tool in environmental impact assessment (EIA). Being synoptic and frequent in coverage, multidate data from Landsat and other satellites provide a reference record and bird’s eye viewing to the environmental situation of the coastal ecosystem and the associated habitats. Furthermore, integration of satellite data with field observations and background information can help in decision if a certain activity has caused deterioration to a specific habitat or not. The present paper is an attempt to utilize remote sensing data for assessment impacts of some human activities on the major sensitive habitats of the north western Egyptian Red Sea coastal zone, definitely between Ras Gemsha and Safaga. Through multidate change analysis of Landsat data (TM & ETM+ sensors), it was possible to depict some of the human infringements in the area and to provide, in some cases, exclusive evidences for the damaging effect of some developmental activities [3]. The coastline of Hurghada has experienced considerable environmental stress from tourist and residential recreational activities. Uncontrolled tourist development has already caused substantial damage to inshore reefs and imbalance in the hydrodynamic pattern of the coastal sediments. The objective of this paper is to investigate environmental changes using multitemporal, multispectral satellite data to identify changes at Hurghada caused by anthropogenic influences. Major detected changes include resort beaches, protection structures and landfill areas; these changes are mainly due to human intervention. Two Landsat Thematic Mapper (TM) images acquired in 1984 and 1997 are used for this analysis. The landfill areas formed during this period are calculated at about 2.15 Km2 . Whilst landfill creates new inexpensive land and improves access to the sea for tourists, it is the cause of environmental problems. In addition, land-use/land-cover and beach changes are determined over the 13-year period [4]. The Red Sea coastal zone is characterized by its sensitive, fragile, unique natural resources and habitats. In the Hurghada coastal region, major changes in the tourism industry have taken place in the last few decades. The detection of environmental changes, in a selected site of the Red Sea coastal zone, will be helpful to protect and develop this coastal environment. A methodology for separating natural and man-made changes in satellite images was developed. It was based on the following assumptions: (1) slow changes, which occur within the range of the class reflectance, represent a natural change rather than an anthropogenic one; (2) natural changes tend to be in the same land-use/land-cover class in each date, i.e. slow changes in the reflectance, not leading to changes in the type of land-use/land-cover class from the master image to the destination one; and (3) rapid changes in the reflectance of the Earth's objects are usually related to anthropogenic activities. This technique is used to identify and assess changes along the coast of Hurghada and Ras Abu Soma, the Red Sea. Results indicate serious human impacts and the necessity for control measures and monitoring. Recommendations are presented [5]. The rapid urban development of the Hurghada area began in early 1980 to build villages and huge tourist resorts and this has continued urban development and subsequent land filling and dredging of the shoreline and the destruction of coral so far. These coastal developments have led to an increase in shoreline land filling and dredging. Despite all the environmental laws of the organization to reduce infringement on the shoreline, the abuses are still ongoing. Change detection analysis using remote sensing is a very good tool to monitor the changes condition in urban development and shoreline. Four sensors was used in this study, three of them are, Landsat Multispectral Scanner (Landsat 1 MSS), Landsat Thematic Mapper (Landsat 5 TM), Landsat Enhanced Thematic Mapper Plus (Landsat 7 ETM+) and the another one is SPOT XS 4 (Originally Système Probatoire de l’Observation de la Terre), acquired during 1972, 1984, 1992, 2004 and 2011, respectively, were used to detect and evaluate Hurghada’s urban expansion and shoreline changes. After the images have been geometrically, radio-metrically and atmospherically corrected using ENVI 5.0 software, the digital number was transformed to the reflectance values and the images were ready to change detection process with the integration of geographic information system using Arc GIS 10 software. The results show that changes during the 39 years of the shoreline is 6.29 km2, (5.65 km2 accretion and 0.64 km2 erosion) and urban development is 16.47 km2 the road network is the 8.738 km2.

The rapid urban development in the Hurghada area since the 1980s has dramatically enhanced the potential impact of human activities. To inventory and monitor this urban development effectively, remote sensing provides a viable source of data from which updated land cover information can be extracted efficiently and cheaply. In this study, data from three satellite datasets, Landsat Thematic Mapper (Landsat 5 TM), Landsat Enhanced Thematic Mapper Plus (Landsat 7 ETM+) and Terra/Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), acquired during 1987, 2000 and 2005, respectively, were used to detect and evaluate Hurghada's urban expansion. Five change detection techniques were tested to detect areas of change. The techniques considered were image differencing, image ratioing, image overlay, multidate principal component analysis (PCA) and post-classification comparison. The post-classification comparison was found to be the most accurate procedure and produced three land use/land cover (LULC) maps of the years 1987, 2000 and 2005 with overall accuracies of 87.8%, 88.9% and 92.0%, respectively. The urban expansion analysis revealed that the built-up area has expanded by 40 km2 in 18 years (1987–2005). In addition, 4.5 km2 of landfill/sedimentation was added to the sea as a result of the coastal urban development and tourist activities. The booming coastal tourism and population pressure were considered to be the main factors driving this expansion, and some natural and artificial constraints constrained the physical shape of the city. The expansion is represented by urban fringe development, linear, infill and isolated models. Topography, lithology and structures were also analysed as possible factors that influenced the expansion. The understanding of the spatial and temporal dynamics of Hurghada's urban expansion is the cornerstone for formulating a view about the future urban uses and for making the best use of the limited resources that are available [1]. A Landsat 5 Thematic Mapper (TM) image of 1987 and a Landsat 7 Enhanced Thematic Mapper Plus (ETM+) image of 2000 were used to examine changes in land use/land cover (LULC) around Hurghada, Egypt, and changes in the composition of coral reefs offshore. Prior to coral reef bottom type classification, the radiance values were transformed to depth invariant bottom indices to reduce the effect of the water column. Subsequently, a multi component change detection procedure was applied to these indices to define changes. Preliminary results showed significant changes in LULC during the period 1987–2000 as well as changes in coral reef composition. Direct impacts along the coastline were clearly shown, but it was more difficult to link offshore changes in coral reef composition to indirect impacts of the changing LULC. Further research is needed to explore the effects of the different image processing steps, and to discover possible links between indirect impacts of LULC changes and changes in the coral reef composition [2]. Knowledge and detecting impacts of human activities on the coastal ecosystem is an essential management requirement and also very important for future and proper planning of coastal areas. Moreover, documentation of these impacts can help in increasing public awareness about side effects of unsustainable practices. Analysis of multidate remote sensing data can be used as an effective tool in environmental impact assessment (EIA). Being synoptic and frequent in coverage, multidate data from Landsat and other satellites provide a reference record and bird’s eye viewing to the environmental situation of the coastal ecosystem and the associated habitats. Furthermore, integration of satellite data with field observations and background information can help in decision if a certain activity has caused deterioration to a specific habitat or not. The present paper is an attempt to utilize remote sensing data for assessment impacts of some human activities on the major sensitive habitats of the north western Egyptian Red Sea coastal zone, definitely between Ras Gemsha and Safaga. Through multidate change analysis of Landsat data (TM & ETM+ sensors), it was possible to depict some of the human infringements in the area and to provide, in some cases, exclusive evidences for the damaging effect of some developmental activities [3]. The coastline of Hurghada has experienced considerable environmental stress from tourist and residential recreational activities. Uncontrolled tourist development has already caused substantial damage to inshore reefs and imbalance in the hydrodynamic pattern of the coastal sediments. The objective of this paper is to investigate environmental changes using multitemporal, multispectral satellite data to identify changes at Hurghada caused by anthropogenic influences. Major detected changes include resort beaches, protection structures and landfill areas; these changes are mainly due to human intervention. Two Landsat Thematic Mapper (TM) images acquired in 1984 and 1997 are used for this analysis. The landfill areas formed during this period are calculated at about 2.15 Km2 . Whilst landfill creates new inexpensive land and improves access to the sea for tourists, it is the cause of environmental problems. In addition, land-use/land-cover and beach changes are determined over the 13-year period [4]. The Red Sea coastal zone is characterized by its sensitive, fragile, unique natural resources and habitats. In the Hurghada coastal region, major changes in the tourism industry have taken place in the last few decades. The detection of environmental changes, in a selected site of the Red Sea coastal zone, will be helpful to protect and develop this coastal environment. A methodology for separating natural and man-made changes in satellite images was developed. It was based on the following assumptions: (1) slow changes, which occur within the range of the class reflectance, represent a natural change rather than an anthropogenic one; (2) natural changes tend to be in the same land-use/land-cover class in each date, i.e. slow changes in the reflectance, not leading to changes in the type of land-use/land-cover class from the master image to the destination one; and (3) rapid changes in the reflectance of the Earth's objects are usually related to anthropogenic activities. This technique is used to identify and assess changes along the coast of Hurghada and Ras Abu Soma, the Red Sea. Results indicate serious human impacts and the necessity for control measures and monitoring. Recommendations are presented [5]. The rapid urban development of the Hurghada area began in early 1980 to build villages and huge tourist resorts and this has continued urban development and subsequent land filling and dredging of the shoreline and the destruction of coral so far. These coastal developments have led to an increase in shoreline land filling and dredging. Despite all the environmental laws of the organization to reduce infringement on the shoreline, the abuses are still ongoing. Change detection analysis using remote sensing is a very good tool to monitor the changes condition in urban development and shoreline. Four sensors was used in this study, three of them are, Landsat Multispectral Scanner (Landsat 1 MSS), Landsat Thematic Mapper (Landsat 5 TM), Landsat Enhanced Thematic Mapper Plus (Landsat 7 ETM+) and the another one is SPOT XS 4 (Originally Système Probatoire de l’Observation de la Terre), acquired during 1972, 1984, 1992, 2004 and 2011, respectively, were used to detect and evaluate Hurghada’s urban expansion and shoreline changes. After the images have been geometrically, radio-metrically and atmospherically corrected using ENVI 5.0 software, the digital number was transformed to the reflectance values and the images were ready to change detection process with the integration of geographic information system using Arc GIS 10 software. The results show that changes during the 39 years of the shoreline is 6.29 km2, (5.65 km2 accretion and 0.64 km2 erosion) and urban development is 16.47 km2 the road network is the 8.738 km2.