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 further analysis. The server side of the system infuses this data into a PostGIS database and displays the road condition on a near real-time basis over a WebGIS. The strength of a good visualization in imparting insight to decision-makers is widely recognized. We advance the paper by assessing procured road data and displaying it in an easy to understand format. In addition to visualization, the WebGIS component also provides for timeline analysis of changes in road conditions, which may help in the improved management of road infrastructure.

Abstract

Barak valley is a region in north east part of India where the practice of shifting cultivation is quite prevalent. Population growth coupled with the geographic isolation of the area have led to an increased pressure on land and a consequent decline in forest cover. The decrease of forests observed is spatially distributed and dependent on neighborhood rules. Hence, we look towards modelling the land use change to understand the land use changes and the factors affecting them. In this paper, we modify an agent-based land use model for modelling shifting cultivation to determine how various policy changes at a larger scale might affect the shifting cultivation practice in the region at the micro level. We explore scenarios like drastic population increase and availability of irrigation infrastructure in the area. Through the scenario analysis we explore how policies play a role in agriculture patterns and influence land use patterns.

Abstract

Terrain, representing features of an earth surface, plays a crucial role in many applications such as simulations, route planning, analysis of surface dynamics, computer graphics-based games, entertainment, films, to name a few. With recent advancements in digital technology, these applications demand the presence of high resolution details in the terrain. In this paper, we propose a novel fully convolutional neural network based super-resolution architecture to increase the resolution of low-resolution Digital Elevation Model (LRDEM) with the help of information extracted from the corresponding aerial image as a complementary modality. We perform the super-resolution of LRDEM using an attention based feedback mechanism named ‘Attentional Feedback Network’ (AFN), which selectively fuses the information from LRDEM and aerial image to enhance and infuse the high-frequency features and to produce the terrain realistically . We compare the proposed architecture with existing state-of-the-art DEM super-resolution methods and show that the proposed architecture outperforms enhancing the resolution of input LRDEM accurately and in a realistic manner.

Abstract

High resolution Digital Elevation Models (DEMs) are an important requirement for many applications like modelling water flow, landslides, avalanches etc. Yet publicly available DEMs have low resolution for most parts of the world. Despite tremendous success in image super resolution task using deep learning solutions, there are very few works that have used these powerful systems on DEMs to generate HRDEMs. Motivated from feedback neural networks, we propose a novel neural network architecture that learns to add high frequency details iteratively to low resolution DEM, turning it into a high resolution DEM without compromising its fidelity. Our experiments confirm that without any additional modality such as aerial images (RGB), our network DSRFB achieves RMSEs of 0.59 to 1.27 across 4 different datasets.

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