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

Abstract

Mangroves are one of the productive and highly adaptive ecosystems on the Earth, and provide invaluable services to the coastal communities. Ecological and sustainable management of the mangrove ecosystem requires crucial knowledge of variability and dynamics over a time and space. There are no reliable recent spatial extent estimates of mangroves or trend of coverage in West coast. The mapping from medium spatial resolution remote sensing data often leads to the underestimation of spatial extent. An attempt has been made to provide detailed information of mangroves species distribution using remote sensing data of high spatial resolution integrated with the other collateral field information through GIS. The coastline changes and the mangrove dynamics during 1989–2010 were assessed using supervised classifier technique provided the spatial distribution of mangrove species namely Rhizophora mucronata, Sonneratia caseolaris, Avicennia officinalis, Sonneratia alba, and Kandelia candel with classification good accuracy. Delineation of mangroves at species wise records based on extensive field data will be invaluable for appropriate management (eg plantation, eco-tourism) and conservation measures for estuaries of Central Western Ghats. Mangroves play pivotal role in providing vital ecosystem goods and services, but are under threat due to anthropogenic activities, affecting habitat for specialised fauna and food resources for humans, birds and fish. This necessitates interventions of regulatory authorities to partner with the local communities in the restoration and conservation of mangrove habitats.

Abstract

One of the major challenges in the current scenario for ecological conservation is to quantify the forest landscape in its spatio-temporal domain and understand further implications of those. While the detailed study of the forest ecosystems may provide insights into biodiversity, carrying capacity and productive nature, most of the studies are restricted to single time/event inventory and focused on assessment of tree diversity patterns. Through the adoption of geospatial technologies like remote sensing and Geographical Information System (GIS), though forest monitoring has been possible, the linkages to the biodiversity distribution and its patterns are still at an empirical level, thus supporting broad measures of protection and preservation without accounting for the local/regional variability.

Abstract

In the recent past, remotely sensed data with high spectral resolution has been made available and has been explored for various agricultural and geological applications. While these spectral signatures of the objects of interest provide important clues, the relatively poor spatial resolution of these hyperspectral images limits their utility and performance. In this context, hyperspectral image enhancement using multispectral data has been actively pursued to improve spatial resolution of such imageries and thus enhancing its use for classification and composition analysis in various applications. But, this also poses a challenge in terms of managing the trade-off between improved spatial detail and the distortion of spectral signatures in these fused outcomes. This paper proposes a strategy of using vector decomposition, as a model to transfer the spatial detail from relatively higher resolution data, in association with sensor simulation to generate a fused hyperspectral image while preserving the inter band spectral variability. The results of this approach demonstrates that the spectral separation between classes has been better captured and thus helped improve classification accuracies over mixed pixels of the original low resolution hyperspectral data. In addition, the quantitative analysis using a rank-correlation metric shows the appropriateness of the proposed method over the other known approaches with regard to preserving the spectral signatures.