Abstract

This paper proposes a system for monitoring of condition and surface of roads in developing countries like India. This system will be used by government agencies to monitor municipal activities like road laying and planning. The system utilizes a database created by geo-citizens or government workers as an input. The heavy machinery in existing systems is not an optimized solution to this problem. Some existing systems use GPS and accelerometer data for determining such artifacts. So, it is evident that there is a need for a system that generates robust, frequent and accountable geo-tagged data. We propose a new collaborative model for such a purpose by fusion of data from multiple sensors hosted on smart-phones of several active geo-citizens. The system focuses mainly on volunteered geographic information, in which users can use their respective smart-phones to collect the data required and upload it for …

Abstract

Current air pollution monitoring systems are bulky and expensive resulting in a very sparse deployment. In addition, the data from these monitoring stations may not be easily accessible. This paper focuses on studying the dense deployment based air pollution monitoring using IoT enabled low-cost sensor nodes. For this, total nine low-cost IoT nodes monitoring particulate matter (PM), which is one of the most dominant pollutants, are deployed in a small educational campus in Indian city of Hyderabad. Out of these, eight IoT nodes were developed at IIIT-H while one was bought off the shelf. A web based dashboard website is developed to easily monitor the real-time PM values. The data is collected from these nodes for more than five months. Different analyses such as correlation and spatial interpolation are done on the data to understand efficacy of dense deployment in better understanding the spatial variability and time-dependent changes to the local pollution indicators.

Abstract

—Real time air pollution monitoring is vital in densely populated areas to assess the micro level air pollution. Monitoring the air quality can be useful for providing accurate information to the public and to help policy decision makers to take actions that can improve the quality of air. Monitoring of the pollutants using Internet of Things (IoT) is a cost effective solution. This has the capability to provide high spatial and temporal air pollutants levels depending on the density of the IoT network. In this paper, we present an end to end prototype solution for monitoring of air pollutants. low cost LoraWAN CO2, CO and PM2.5 sensors are used in this prototype application. We also implemented a real time monitoring system using web dashboards with the capability to perform real time analysis using open source software elasticsearch and kibana. A prototype web dashboard showing the real time values of the sensor values along with temperature and humidity values is also developed. Index Terms—Air pollutants, Real time, Visualization, Analysis

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.

Abstract

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.

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

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.