Abstract

Data fusion is a prevalent method to extract the best combination of satellite images from different modalities - spectral,spatial, temporal. A new method of object-based fusion of high resolution multi spectral (MS) and panchromatic (PAN)images is proposed in this paper, which emphasizes on spectral characteristics preservation. It is a hybrid approach where individual objects detected in the images are considered for mapping data and information transfer is done on a per-pixel basis. In addition, the paper proposes a quantitative assessment measure to assess the spectral distortion of the fused outcomes. The quantitative results demonstrate that the pro-posed method is better in terms of preserving spectral characteristics as compared to other widely used methods such as Principle Component (PC) based fusion, Intensity Hue Saturation (IHS) based fusion and Color Normalization (CN) or Brovey

Abstract

This article attempts to look at the manner in which the leftwing in Indian politics has dealt with the globalisation of India's economy and the consequent changes in foreign policy over the two decades since 1991. The left in India opposed the new economic policies, of which globalisation was a central aspect, and worked to build a wide consensus against it within the polity. The left's opposition to globalisation was premised on the fact that it would lead to a watering-down of India's sovereignty and will lead to the imposition of economic and foreign policies that would be detrimental to the nation. Despite deep internal differences on a range of issues within the left in India, this was a shared understanding of what globalisation meant and what its consequences would be. This article looks at how the left built up its opposition to globalisation of the Indian economy and how has it steered this opposition over the two decades since. It argues that the left's analysis of both the major characteristics of globalisation as well as its consequences for India's economy and foreign policy have proved to be grossly inadequate, if not entirely erroneous. However, despite mounting evidence that globalisation was not a disaster for the Indian economy, nor did it whittle down the State's sovereignty, there was no revision in the left's analysis or political stand. The article argues that the Indian radical has been unable to come to terms with or define globalisation, and remains unsure of how to engage with it. This has led to theoretical confusions, which have opened the door to the left's hegemonisation by the idea of nationalism, to the extent where its positions often become indistinguishable from the radical right. The article concludes by suggesting that this failure to analyse and engage with globalisation has been both a symptom as well as the cause of the weakening of the leftwing in Indian politics

Abstract

Unplanned large scale developmental projects in recent times have been causing alterations in land use land cover [LULC] at spatial and temporal scales with the substantial changes in the landscape. Uttara Kannada district has the distinction of having highest forest cover in India.Forest ecosystems in the district have witnessed major transformations due to industrialization during the post-independence. Impact of developmental activities during the post-independence era, is evident from barren hill tops due to deforestation. The analysis of temporal spatial data acquired through space borne sensors highlights the decline of evergreen-semi evergreen forest cover from 67.73% (1973) to 32.08% (2013). The ad-hoc approaches adopted in the implementation of developmental projects in the ecologically sensitive regions has impaired the ecosystem services affecting the people’s livelihood. The changes in the landscape structure with LULC changes have altered the functional abilities of an ecosystem evident from lowered hydrological yield, disappearing perennial streams, higher instances of human–animal conflicts, declined ecosystem goods, etc. About 64355 Ha of forest land is diverted for various non-forestry activities during the last four decades by the government apart from encroachment of 7072 Ha of forest area for agriculture, horticulture activities, etc. Encroachment of forest land has resulted in the land degradation leading to reduce dproductivity. Alterations in bio-geo chemical and hydrological cycles have necessitated the restoration of native forests in the region to ensure water and food security, and livelihood of the local people. Monitoring and visualization of landscape dynamics helps in evolving appropriate manage-ment strategies. LULC dynamics analyses considering agents (developmental projects) through fuzzy analytic hierarchy process (AHP) integrated with Markov cellular automata (CA) indicate the developmental projects such as West Coast Paper Mill (WCPM) and Kaiga Nuclear PowerHouse (KNPH) will lead to further fragmentation of forests with spurt in urbanization by 2022.This necessitates framing appropriate policy measures to sustain natural resources focusing on the landscape’s ecological, hydrological, economic and social factors.

Abstract

This paper describes a software system for automatically generating a reference (baseline) building energy model from the proposed (as-designed) building energy model. This system is built using the OpenStudio Software Development Kit (SDK) and is designed to operate on building energy models in the OpenStudio file format.

Abstract

The radiant systems consume less energy and provide better thermal comfort as compared to the Conventional system, but the main drawback of this technology is condensation and inability to cater the latent load. These challenges have directed many researchers to move their emphasis towards Personalized Conditioning System which aims to generate a microclimate zone near the vicinity of occupant according to need. This study is focused on Personalized Conditioning System when it is combined with a radiant system. The objectives of this study are 1) Evaluate the performance analysis of the system in cooling mode and, 2) Evaluate the performance of PRCS for cooling mode on different radiant cooling systems. An experimental setup of an office cubicle has been designed and fabricated using radiant panels at MNIT Jaipur, India. Three cases of the experiment were designed to evaluate and compare the performance of the system which are: Only Radiant Cooled Ceiling System (RCCS), Only Personalized Radiant Cooling System (PRCS), and both PRCS and RCCS coupled. It was observed that PRCS alone is sufficient to reach the thermal comfort criteria with low energy consumption, but it was unable to maintain the background temperature. When PRCS coupled with RCCS, it gives maximum thermal comfort for high supply water temperature ranging from 17 °C to 21 °C.

Abstract

Energy utilization can be improved by precise plug load monitoring and control. Plug load energy consumption is nearly 30% of the total building energy consumption. Therefore, plug load identification is a key requirement for energy conservation in buildings. Intrusive load monitoring techniques identify loads precisely but have not been tested widely so far for their performance in changing operating conditions. Hence, the present research proposes a robust low frequency intrusive load monitoring technique to identify load accurately. A smart power strip using proposed load identification technique is designed and developed. Linear regression is applied on the acquired data to capture the behavioral trends of a particular device more explicitly and concisely. Further, weighted K-NN classifier is applied on the transformed data set for device. Experimental results show that the proposed algorithm performs better than the standard classifiers, and can offer tangible savings.

Abstract

Pervasive large scale land cover land use (LULC) changes leading to deforestation have enhanced concentrations of carbon dioxide in the atmosphere and significantly impacted the regional water cycle and bio-geo chemical cycles. This neceissates assessment of current level of transitions and likely future transitions for an effective decision making towards sustainable management of natural resources. Karwar taluk of Central Western Ghats has been experiencing rapid landscape changes due to unplanned developmental activities and the consequences were evident form frequent occurrence of landslides with the loss of life and property. Analysis of land cover transitions based on temporal spatial data helps more efficient land management policies. The present study aims to visualize and forecast forest land status (2000-2026) by exploring the effects of various factors such as protected areas, slope, waterbodies, proximity to roads, industries and urban centers through agent based model on land use change. The land use change inducing factors are normalized and relative weights were generated through fuzzyfication and Multi criteria Evaluation (MCE). The spatial patterns were quantified using metrics such as Number of Patches (NP), the Edge Density (ED) and Area-Weighted Mean Patch Fractal Dimension (AWMPFD). The forest cover has reduced from 73.59 % (2000) to 67.32% (2016) with an increase in built-up from 3.17 (2000) to 8.65% (2016). Forest cover in 2024 would be about 60.44% with increase in built-up area 14.79%. Unplanned developmental activities with the increasing trends of urbanisation are the prime reasons for disasters such as landslides, water scarcity, etc. The technique has provided insights to unsustainable land cover transitions in the region for an integrated and holistic path of development to sustain natural resources while mitigating human made disasters.

Abstract

The current methods of object segmentation and extraction and classification of aerial LiDAR data is manual and tedious task. This work proposes a technique for object segmentation out of LiDAR data. A bottom-up geometric rule based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. For curved wall surfaces, comparison of localized surface normals was done to segment buildings. The algorithm has been applied to both synthetic datasets as well as real world dataset of Vaihingen, Germany. Preliminary results show successful segmentation of the buildings objects from a given scene in case of synthetic datasets and promissory results in case of real world data. The advantages of the proposed work is non-dependence on any other form of data required except LiDAR. It is an unsupervised method of building segmentation, thus requires no model training as seen in supervised techniques. It focuses on extracting the walls of the buildings to construct the footprint, rather than focussing on roof. The focus on extracting the wall to reconstruct the buildings from a LiDAR scene is crux of the method proposed. The current segmentation approach can be used to get 2D footprints of the buildings, with further scope to generate 3D models. Thus, the proposed method can be used as a tool to get footprints of buildings in urban landscapes, helping in urban planning and the smart cities endeavour.

Abstract

Relational databases have been around for a long time and spatial databases have exploited this feature for close to two decades. The recent past has seen the development of NoSQL non-relational databases, which are now being adopted for spatial object storage and handling, too. While SQL databases face scalability and agility challenges and fail to take the advantage of the cheap memory and processing power available these days, NoSQL databases can handle the rise in the data storage and frequency at which it is accessed and processed - which are essential features needed in geospatial scenarios, which do not deal with a fixed schema(geometry) and fixed data size. This paper attempts to evaluate the performance of \ an existing NoSQL database ’MongoDB’ with its inbuilt spatial functions with that of a SQL database with spatial extension ’PostGIS’ for two problems spatial and aggregate queries, across a range of datasets, with varying features counts. All the data in the analysis was processed In-memory and no secondary memory was used. Initial results suggest that MongoDB performs better by an average factor of 10x-25x which increases exponentially as the data size increases in both indexed and non-indexed operations. Given these results, NoSQL databases may be better suited for simultaneous multiple-user query systems including Web-GIS and mobile-GIS. Further studies are required to understand the full potential of NoSQL databases across various geometries and spatial query types.

Abstract

Current methods of building segmentation from LiDAR depends on extraction of roof planes to segment the buildings. In our work, we propose a new method to extract buildings using the wall facades. The motivation of using wall facades is based on the rise of terrestrial LiDAR compared to aerial LiDAR. The shape property is the most important information obtained from a LiDAR file. We try to use this shape property from the LiDAR file to segment buildings from the given scene. In the algorithm, the non-ground return points parallel to a facade of the potential building are combined to form multiple slices. These slices are parallel to the facade of a potential building. These slices are then merged based on the shape similarity and proximity to generate the footprint of the building. After generating the footprint of the building, we can also reconstruct the 3D model of the given building. The algorithm was tested both on simulated synthetic data containing different buildings and real world benchmark datasets. The results show complete segmentation of buildings from the synthetic datasets and promissory results from the real world datasets. The algorithm proposed is versatile in nature and can be easily adapted to different datasets with minimal modifications and tweaking. Existing works involve the use of secondary datasets like satellite images, orthophotos etc in order to aid in segmentation. Our work uses only the LiDAR data and no other secondary data to identify the buildings from the scene. Demonstration have been shown to segment partial buildings in a given scene as well. Thus, the novelty of the proposed work is non-dependence on any other form of data required except LiDAR. The applications of segmenting buildings from a given LiDAR scene can be used in urban planning, resource management and monitoring tasks.