Abstract

Analysis of high variability of inter-state land use land cover change using remote sensing and GIS techniques primarily agriculture in Krishna river basin, India.

Abstract

By 2050 most parts of India will be water stressed zones as most of the water resources are under heavy stress due to increasing nutrient contamination in their waters. In this scenario, studying the changes occurring in the freshwater nutrient contamination levels over a temporal scale is extremely important. This study focuses on monitoring the changes occurring in the nutrient contamination levels over a decade in a large reservoir known as Nagarjuna Sagar (NS) using remote sensing data. In this study, Landsat (5 & 8) data for the year 2005, 2009, 2015 and Sentinel (2A and 2B) data for the years 2016 and 2018 is used to study nutrient contamination in NS. The spatial spread of chlorophyll-a (chl-a) area is used as a proxy to estimate the extent of nutrient contamination in NS. In this study, only October images of NS are used as they exhibit the maximum spatial spread of Chl-a and hence help assess the contamination levels over the period 2005-2018. The analysis shows that during this period, chl-a spatial spread area has increased from 21 Km2 to 205 Km2, indicating a decrease in water quality in the reservoir. The study shows that this is accompanied by an increase in the agricultural land use area by 1000 Km2 in addition to a steep increase in the use of agricultural inputs, primarily fertilisers like urea, P and K. Thus, while the combined effect of excessive usage of fertilizers with agricultural intensification has increased crop yields, it has also contributed to damaging the freshwater resources.

Abstract

Geospatial data capture and handling of indoor spaces is increasing over the years and has had a varied history of data sources ranging from architectural and building drawings to indoor data acquisition approaches. While these have been more data format and information driven primarily for the physical representation of spaces, it is important to note that many applications look for the semantic information to be made available. This paper proposes a space classification model leading to an ontology for indoor spaces that accounts for both the semantic and geometric characteristics of the spaces. Further, a Space semantic model is defined, based on this ontology, which can then be used appropriately in multiple applications. To demonstrate the utility of the model, we also present an extension to the IndoorGML data standard with a set of proposed classes that can help capture both the syntactic and semantic components of the model. It is expected that these proposed classes can be appropriately harnessed for use in diverse applications ranging from indoor data visualization to more user customised building evacuation path planning with a semantic overtone. View Full-Text

Abstract

Road networks are the lifeline of a city and understanding its usage has a number of potential applications from transportation planning and engineering aspects to environmental management. While the full network is important to be analyzed for such applications, prudent planning needs one to identify the significant sections of the road network and prioritize them. Annual Average Daily Traffic (AADT), an estimate of the average daily traffic along a defined road segment, is one such data that helps in such endeavors. But, roads are also about connectivity and accessibility across different regions. Hence, this paper proposes a study that integrates the AADT data with implicit information derived from the road network to generate a computationally representative (CoRe) well connected sub-network, significantly smaller than the original network. While the AADT data analysis looks for road segments with high traffic, this paper proposes and evaluates a graph theory based approach for calculating road priorities purely based on the topological structure of the road network. The work further demonstrates the utility of the CoRe sub-network in terms of both, achieving gains in path computation and capturing the behavioral pattern of travelers. A case study of the Melbourne, Australia supports the feasibility and applicability of this knowledge integration approach.

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In this paper, a novel technique is presented to detect buildings from very high resolution satellite image. This work builds on the learning of ICA based building detection technique from the very high resolution (VHR) multispectral satellite images presented in [1]. The candidate building pixels obtained through ICA are used to extract maximally stable extremal regions (MSER) which are then filtered using geometric properties to obtain final potential buildings. The technique is aimed at reducing false detection at pixel-level and improving object-level performance of [1]. Combining the two works offers an unsupervised building detection technique which is robust towards size, shape, color, types of rooftops and shadows. A wider test image set consisting of 15 images of different dimensions are used to evaluate performance of the complete detection process. The combined technique achieves object-level precision and recall of 80.64% and 83.65% respectively.

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In many cities, it is difficult for authorities to build road monitoring systems that are cost-friendly and effective. The existing road monitoring systems include heavy machinery with sensors fitted which make them expensive for authorities to use for frequent monitoring. To address this challenge, we propose an innovative collaborative system for road condition monitoring leveraging the existing technologies by data fusion approach. The mobile application on smart-phones of geo-citizens coordinates the data collection and reports back to a central server for assimilation and further processing. Image data is processed to obtain the road surface while the accelerometer and GPS data is used for identifying road condition and location information. This data is then fused based on location and timestamp information and stored as segments in the database. As the data obtained is crowd-sourced, there are multiple tracks for the same segment of the road and this segmented approach allows for collating multiple user-provided information and validation. In case there is multiple track information available for a particular segment, a ranking model based on the number of collaborators is used to rank the status. The final database holds the information like segmentId, timestamp, GPS coordinates and status of the segment. This data serves as an input to the visualization service. The multiplicity of data points and anonymity of the users establishes robustness and crowd-sourced model ensures frequent and continuous coverage.

Abstract

In many cities, it is difficult for authorities to build road monitoring systems that are cost-friendly and effective. The existing road monitoring systems include heavy machinery with sensors fitted which make them expensive for authorities to use for frequent monitoring. To address this challenge, we propose an innovative collaborative system for road condition monitoring leveraging the existing technologies by data fusion approach. The mobile application on smart-phones of geo-citizens coordinates the data collection and reports back to a central server for assimilation and further processing. Image data is processed to obtain the road surface while the accelerometer and GPS data is used for identifying road condition and location information. This data is then fused based on location and timestamp information and stored as segments in the database. As the data obtained is crowd-sourced, there are multiple tracks for the same segment of the road and this segmented approach allows for collating multiple user-provided information and validation. In case there is multiple track information available for a particular segment, a ranking model based on the number of collaborators is used to rank the status. The final database holds the information like segmentId, timestamp, GPS coordinates and status of the segment. This data serves as an input to the visualization service. The multiplicity of data points and anonymity of the users establishes robustness and crowd-sourced model ensures frequent and continuous coverage.

Abstract

Barak valley is an area in northeast part of India where traditionally practice of shifting cultivation is more prevalent than sedentary agriculture. In addition, population growth coupled with the geographic isolation of the area has further aggravated the decline of forest cover in the area. The three districts of Assam that cover the Barak valley show varied land covers and also has very different population densities. This paper attempts to model the land use changes in the Barak valley over a period of 1988 to 2005, using an agent-based model. Each district is modelled as an agent of change here to capture the interactions between the various land uses and to decide on the resource allocation over the district to be allotted to shifting cultivation. The model accounts for both economic as well as geographic factors, like access to infrastructure while making the land use decision on a year-on-year basis. The model considers the need for shifting cultivation area for both a staple crop - rice and a non-staple crop based on the demand against the supply estimated from irrigated and rain-fed crop regions. While the model has been tuned based on the data till 1997, the simulated model outcome of 2005 is evaluated against an existing remote sensing derived land use map of the region and it is found to achieve an accuracy of about 85%. While the aggregated results across the region show good concurrence, the randomness inherent in the choice of the shifting cultivation land limits the ability to predict the precise locations. The paper will discuss the challenges including scale issues in model application for a region as diverse as Barak.

Abstract

pgRouting library provides functions to compute shortest path between any two points of a road network which is of great demand and also a topic of interest in the field of GIS, graph theory and transportation. To compute path in a road network, pgRouting functions process the entire road network which is a major bottleneck when it comes to routing in large road networks leading to the requirement of large server resources. A reduction/compression in the input network that is to be processed for path computation would improve the performance of pgRouting. In this study a map generalization based network model is proposed which extracts a significantly smaller subset of the road network aka skeleton which further used to divide the network into zones, that shall be selectively used in path computation. This results in processing a much smaller part of the network to compute path between any two points leading to an overall improvement in query performance of pgRouting when computing path, especially on large road networks. As part of assessment of this approach and its applicability to large road networks, the paper presents an in-depth analysis of the trade-offs between deviation in computed path and the performance gain in terms of space and time on road networks of varying sizes and topology to get a better understanding for both providing a sound proof of the utility of the proposed method and also to show its implementability within the current model of pgRouting or any other routing platforms.

Abstract

Forest inventory parameters, primarily tree diameter and height, are required for several management and planning activities. Currently, Terrestrial Laser Scanning (TLS) is a promising technology in automated measurements of tree parameters using dense 3D point clouds. In comparison with conventional manual field inventory methods, TLS systems would supplement field data with detailed and relatively higher degree of accurate measurements and increased measurement frequency. Although, multiple scans from TLS captures more area, they are resource and time consuming to ensure proper co-registration between the scans. On the other hand, Single scans provide a fast and recording of the data but are often affected by occlusions between the trees. The current study evaluates potential of single scan TLS data to (1) develop an automatic method for tree stem identification and diameter estimation …