India has experienced significant Land-Use and Land-Cover Change (LULCC) over the past few decades. In this context, careful observation and mapping of LULCC using satellite data of high to medium spatial resolution is crucial for understanding the long-term usage patterns of natural resources and facilitating sustainable management to plan, monitor and evaluate development. The present study utilizes the satellite images to generate national level LULC maps at decadal intervals for 1985, 1995 and 2005 using onscreen visual interpretation techniques with minimum mapping unit of 2.5 hectares. These maps follow the classification scheme of the International Geosphere Biosphere Programme (IGBP) to ensure compatibility with other global/regional LULC datasets for comparison and integration. Our LULC maps with more than 90% overall accuracy highlight the changes prominent at regional level, i.e., loss of forest cover in central and northeast India, increase of cropland area in Western India, growth of peri-urban area, and relative increase in plantations. We also found spatial correlation between the cropping area and precipitation, which in turn confirms the monsoon dependent agriculture system in the country. On comparison with the existing global LULC products (GlobCover and MODIS), it can be concluded that our dataset has captured the maximum cumulative patch diversity frequency indicating the detailed representation that can be attributed to the on-screen visual interpretation technique. Comparisons with global LULC products (GlobCover and MODIS) show that our dataset captures maximum landscape diversity, which is partly attributable to the on-screen visual interpretation techniques. We advocate the utility of this database for national and regional studies on land dynamics and climate change research. The database would be updated to 2015 as a continuing effort of this study.

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.

This paper presents the analysis of water supply behavior within an educational campus, serving as a use-case to demonstrate the broader applicability of an innovative IoT-based framework integrated with deep learning techniques. By retrofitting analog water meters with IoT devices, the study captures images of meter dials, which are then locally processed using a deep learning-based digit detection algorithm. This process converts the images into digits and transmits the data to the cloud for real-time analysis, thereby enhancing the accuracy and reliability of water usage data. Focusing on two key regions within the campus—student hostels and faculty/staff quarters—the analysis thoroughly examines the impact of water supply patterns on both a monthly and weekly basis. It reveals how the distinct characteristics of each month, such as holidays, exams, and class schedules, significantly influence water consumption in these areas. The study particularly highlights the variations in water usage in student hostels, driven by the academic calendar and student lifestyle, in contrast to the more stable water demand observed in faculty/staff quarters. The integration of the data refinement algorithm uncovers the underlying consumption patterns within these campus residence. The findings from this detailed investigation are instrumental in understanding the water distribution patterns, particularly within Integrated Water Systems (IWS), and set a precedent for the potential scalability and adaptability of the framework. This study not only sheds light on the specific water management needs of an educational campus but also suggests that the successful application of this system in such a dynamic and varied setting indicates its potential for broader application, thereby contributing to more informed decision-making and promoting sustainable water management practices in various contexts.

This research develops an agent-based land-use model for shifting cultivation, where the spatial distribution of crop cultivation is dynamically determined by the relationship between demand and supply of crops. We apply and evaluate the model using statistical and geographic data from Luangprabang Province, Laos, where rice and other crops are cultivated in various forms, including shifting cultivation. Our model explicitly incorporates socioeconomic dimensions of shifting cultivation in which villages are assigned the role of decision makers. We evaluate the model by comparing the simulation results with the existing statistical data and remote sensing images from the 1990s. Our model provides reasonably satisfactory estimates of aggregate area and volume of each crop type at the provincial level. We also evaluate the model across differing spatial resolutions for shifting cultivation areas. We find that the model has limited explanatory power at higher spatial resolutions of 0.5 km to 2.5 km grid cells, but can account for the spatial patterns fairly well at more aggregate levels with the resolutions of 5 km to 10 km.

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.