Abstract

LANDSAT images are used to identify organic contaminants in water bodies, but, there is no enough evidence in present literature that LANDSAT is also good in identifying a mixture of organic and mineral contaminants such as agricultural waste. The focus of this paper is to evaluate the effectiveness of LANDSAT imagery to identify organic and mineral contamination (OMC) and to identify spread extent variations of pollution over the season/year in the Nagarjuna Sagar (NS) reservoir using only satellite images. A new band combination is proposed in order to detect OMC, because existing formulae based on band ratio proved to be inadequate in detecting the contamination in NS. Difference in reflectance values of Red and Green channel of an image helps clearly distinguish clear water from OMC water. This procedure was applied over LANDSAT data of the calendar years 2008, 2014 and 2015 to understand the contamination spread pattern through the reservoir. Results show that contamination is following a similar pattern over these calendar years. In January contamination starts at inlets and by May contamination spreads to almost 90% of the reservoir when the total area of water spread is also reduced by half. Contamination spread is low during the monsoonal period of June to September due to heavy inflow and heavy outflow of waters from NS reservoir. Post monsoon NS is contaminated again because of heavy inflow of runoffs from neighboring land use and limited water outflow. This contamination spread pattern matches the agricultural seasons and fertilizer application pattern in this region, indicating that agricultural use of fertilizers could be one of the primary causes of contamination for this waterbody.

Abstract

Relational databases have been around for a long time, and Spatial databases have exploited this feature for close to two decades. The recent past has seen the development of NoSQL non-relational databases, which are now being adopted for spatial object storage and handling too. Moreover, this is gaining ground in the context of increased shift towards Geospatial Web Services on both the Web and mobile platforms especially in the user-centric services, where there is a need to improve the query response time. This paper attempts to evaluate the performance of an existing NoSQL database ‘MongoDB’ with its inbuilt spatial functions with that of an SQL database with spatial extension ‘PostGIS’ for two fundamental spatial problems—line intersection and point containment problem, across a range of datasets, with varying feature counts. Given these results, NoSQL databases may be better suited for simultaneous multiple-user query systems including Web-GIS and mobile-GIS. Further studies are required to understand the full potential of NoSQL databases across various geometries and spatial query types.

Abstract

The traditional pixel-based classification totally relies on spectral information and neglects the spatial information. These methods when applied on very high-resolution imagery get confused because of the increased variability implicit within the data and thus leads to lower classification accuracies.The object-based image analysis (OBIA) is advantageous to deal with objects that are composed of homogeneous pixels.This paper aims at automatically extracting buildings from very high-resolution satellite imagery using Object Based Image Analysis(OBIA).The algorithm uses an active contour model called chan-vese segmentation to create objects from the image. Objects representing vegetation or trees are removed by subtracting NDVI mask from the segmented output. The detected objects are further filtered based on regional properties like minimum area, width of object etc. The results are promising with 74-77%of the buildings getting detected as objects

Abstract

We have addressed the problem of visualization of vector building data on top of a terrain. It tries to make it more realistic by automated draping of building textures from geo-tagged images which are captured from a cell phone camera with a built-in GPS. We use the properties of the images to tag them to the corresponding 2D polygon footprint by using the camera pose, and the cameras position to automate our process. The elevation data captured from NASAs SRTM project, GTOPO, etc can be used to render 3D terrains using existing GPU based Level of detail(LOD) algorithms[1] [2][3]. The vector data of buildings along with their height at-tributes can be obtained from various Lidar based algorithmsor from DEM data[4] [5]. A scene graph is used as the data structure which is used to render these vector based graphics models

Abstract

This study identifies spaces vulnerable to a disaster in terms of impedance offered to evacuation. Evacuation for spaces is evidently dependent on combination of several spatia and demographic features. In order to develop an evacuation model or prioritize regions for special attention during calamity, it is necessary to understand interactions and interdependencies of numerous factors. In this study,we address vulnerability issue in terms of potential difficulties in evacuating a region from a spatial perspective. Arrangement of built-up areas and interactions of neighborhoods are studied based on topological as well as metric distances between them. We couple building to building (point to point) accessibility considering metric distances & their respective on-ground areas with space syntax based axial analysis on urban streets.The approach creates multidimensional feature vector on top of buildings layer.Feature vectors consist of building on-ground areas,their reach,betweenness, and local integration & choice values of segments adjacent to which they are located for a metric radius of 500 meters and topological radius R10. Further, we use bivariate Local Indicators of Spatial Association(LISA) to identify the clusters and conclusively carry out knowledge based denomination of the areas in terms of their vulnerability. The study reveals that road segments offering highest traffic impedance for planned grid like arrangements are parallel, of equal length and in close vicinity of each other whereas for non – grid wards are rather scattered. The clusters of buildings located on roads with lower accessibility are significantly less in number as well as smaller sized for grid like symmetries compared to non–grid arrangements. The evaluation of areas from vulnerability perspective carried out in this study can form the basis of a generalized decision support system– framework to identify, rank and prioritize both the current and future space planning and emergency response

Abstract

With the increased shift towards GeoSpatial Web Services on both the Web and mobile platforms especially in the usercentric services, there is a need to improve the query response time. The traditional routing algorithm requires server to process the query and send the results to a client but here we are focussing on query processing within the client itself. This paper attempts to evaluate the performance of an existing NoSQL database and SQL database with respect to routing algorithm and evaluate whether or not we can deploy the computations on the client system only. While SQL databases face the challenges of scalability and agility and are unable to take the advantage of the abundant memory and processing power available these days, NoSQL databases are able to use some of these features to their advantage. The nonrelational databases are more suited for handling the dynamic rise in the data storage and the increased frequency of data accessibility. For this comparative study, MongoDB is the NoSQL engine while the PostgreSQL is the chosen SQL engine. The dataset is a synthetic dataset of road network with several nodes and we find the distance between source and destination using various algorithms. As a part of paper The implementation we are planning on using pgRouting for the analysis which currently uses PostgreSQL at the backend and implements almost all the routing algorithms essential in practical scenarios. We have currently analyzed the performance of NoSQL databases for various spatial queries and have extended that work to routing.

Abstract

Large area cityGML visualization currently deals with its conversion to another model such as VRML, X3D or KML data format which can be visualized using various visualization tools like Google Earth, VTP, Cesium, NASAs World Wind, etc. But these conversion to VRML or X3D, leads to a loss of the geographical information, geometries, which restricts the aspect of geospatial analysis, in addition to adding an extra preprocessing step. One of the tool that supports 3D visualization of cityGML data is Aristoteles developed by the Institute for Cartography and Geoinformation. But it lacks features like move around camera controls, projecting the coordinates from the cityGML CRS, and underneath terrain rendering. On the other hand virtual globes like NASAs world wind or cesium globe, are powerful enough to harness the power of the systems GPU to render large amount of geometries and DEM data to generate terrains, give scene controls, etc, but doesn't give support for visualization of cityGML data in its current form. Thus there is a need to integrate the virtual globe technology and the visualization functionality of tools like Aristoteles, so as to render buildings on top of terrain and give a more realistic view of the environment.

Abstract

Public consultation is an important decision-aiding process in environmental impact assessment (EIA) and aids in building up consensus between various stakeholders, primarily the local public. In this meta-analysis, proceedings of 100 public hearings (PHs) recorded in the Indian state of Gujarat were analysed for the views of local public between environmental issues and others while an industry is being set up across five sectors – bulk drug and drug intermediates, cement, highway projects, oil and gas exploration and thermal power plants. The analysis shows that environmental issues are only 33% of the total issues raised, while socio-economic, infrastructure, PH process, track record and other general issues cover 21%, 13%, 2%, 12% and 19%, respectively. This implies that irrespective of sector or project local socio-economic and developmental concerns outweigh environmental issues and the current PH process is not able to get the appropriate inputs and insights from the stakeholders in improving the environmental decision-making. In light of these, some alternatives for strengthening the EIA-PH process is proposed in the paper

Abstract

Road infrastructure in countries like India is expanding at a rapid pace and is becoming increasingly difficult for authorities to identify and fix the bad roads in time. Current Geographical Information Systems (GIS) lack information about on-road features like road surface type, speed breakers and dynamic attribute data like the road quality. Hence there is a need to build road monitoring systems capable of collecting such information periodically. Limitations of satellite imagery with respect to the resolution and availability, makes road monitoring primarily an on-field activity. Monitoring is currently performed using special vehicles that are fitted with expensive laser scanners and need skilled resource besides providing only very low coverage. Hence such systems are not suitable for continuous road monitoring. Cheaper alternative systems using sensors like accelerometer and GPS (Global Positioning System) exists but they are not equipped to achieve higher information levels. This paper presents a prototype system MAARGHA (MAARGHA in Sanskrit language means an eternal path to solution), which demonstrates that it can overcome the disadvantages of the existing systems by fusing multi-sensory data like camera image, accelerometer data and GPS trajectory at an information level, apart from providing additional road information like road surface type. MAARGHA has been tested across different road conditions and sensor data characteristics to assess its potential applications in real world scenarios. The developed system achieves higher information levels when compared to state of the art road condition estimation systems like Roadroid. The system performance in road surface type classification is dependent on the local environmental conditions at the time of imaging. In our study, the road surface type classification accuracy reached 100% for datasets with near ideal environmental conditions and dropped down to 60% for datasets with shadows and obstacles

Abstract

In the recent past there is increased interest in detection and extraction of buildings using object based approaches on given high to very high resolution imagery. In this paper, we introduce a new unsupervised approach to detect buildings from the very high resolution (VHR) multispectral satellite image. Independent component analysis (ICA) followed by Otsu thresholding is used for extraction of multicoloured buildings of diversified size and shape. QuickBird imageof Legaspi city has been used to analyze the technique and detection results obtained from three subset images indicate average detection percentage of 84.35 % along with 38.19 % branch factor value. Object-level evaluation results give 72.48% for fully detected buildings count