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OBJECTIVE: This study aims to investigate comparative severity analysis of motorized two-wheeler (MTW) crashes based on drivers' liability using police-reported a crash data base. METHODS: Using crash data from 2016 to 2020, this study examines and analyses the key factors affecting the severity of MTW injuries in Dehradun. For analysis, the ordinal logistic approach is used because severity levels are attributed with natural ordering. Differentiating from past studies, this research distinguished between collisions in which MTW crashes were considered as first party (crash in which MTW rider is accountable) and second person (crash in which MTW rider is a victim). RESULTS: Result suggests that age, pillion passenger, type of collision, road network, and impacting vehicle increase the seriousness of a crash in both cases. However, crash day, crash time, and light condition were found to be significant in the case of second-party crashes. Similarly, crash seriousness tends to decrease in first-party crashes, whereas it increases in the context of second-party crashes. CONCLUSION: The statistical results were correlated with past studies to provide proper justification in order to provide a better understanding of small-displacement MTW fatal accidents in developing countries. Additionally, this research aids in the development of mitigation strategies and future research directions to improve the safety of MTW users.

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Land Use Land Cover (LULC) classification is pivotal to sustainable environment and natural resource management. It is critical in planning, monitoring, and management programs at various local and national levels. Monitoring changes in LULC patterns over time is crucial for understanding evolving landscapes. Traditionally, LULC classification has been achieved through satellite data by remote sensing, geographic information system (GIS) techniques, machine learning classifiers, and deep learning models. Semantic segmentation, a technique for assigning land cover classes to individual pixels in an image, is commonly employed for LULC mapping. In recent years, the deep learning revolution, particularly Convolutional Neural Networks (CNNs), has reshaped the field of computer vision and LULC classification. Deep architectures have consistently outperformed traditional methods, offering greater accuracy and efficiency. However, the availability of high-quality datasets has been a limiting factor. Bridging the gap between modern computer vision and remote sensing data analysis can revolutionize our understanding of the environment and drive breakthroughs in urban planning and ecosystem change research. The "Sen-2 LULC Dataset" has been created to facilitate this convergence. This dataset comprises of 213,761 pre-processed 10 m resolution images representing seven LULC classes. These classes encompass water bodies, dense forests, sparse forests, barren land, built-up areas, agricultural land, and fallow land. Importantly, each image may contain multiple coexisting land use and land cover classes, mirroring the real-world complexity of landscapes. The dataset is derived from Sentinel-2 satellite imagery sourced from the Copernicus Open Access Hub (https://scihub.copernicus.eu/) platform. It includes spectral bands B4, B3, and B2, corresponding to red, green, and blue (RGB) channels, and offers a spectral resolution of 10 m. The dataset also provides an equal number of mask images. Structured into six folders, the dataset offers training, testing, and validation sets for images and masks. Researchers across various domains can leverage this resource to advance LULC classification in the context of the Indian region. Additionally, it catalyzes fostering collaboration between remote sensing and computer vision communities, enabling novel insights into environmental dynamics and urban planning challenges.

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Barren lands are being transformed into agricultural fields with the growing demand for agriculture-based products. Hence, monitoring these regions for better planning and management is crucial. Surveying with high-resolution RS (remote sensing) satellites like Worldview-2 provides a faster and cheaper solution than conventional surveys. In the study, the arid region comprising cropland and barrenlands are efficiently and autonomously delineated using its spectral and textural properties using state-of-the-art random forest (RF) ensemble classifiers. The textural information window size is optimized and at a GLCM (gray-level co-occurrence matrix) window size of 13, a stable trend in classification accuracy was observed. A further rise in window sizes did not improve the classification accuracy; beyond GLCM 19, a decline in accuracy was observed. Comparing GLCM-13 RF with the no-GLCM RF classifier, the GLCM-based classifiers performed better; thus, the textural information assisted in removing isolated crop-classified outputs that are falsely predicted pixel groups. Still, it also obscured information about barren lands present within croplands. Delineation accuracy was 93.8 % for the no-GLCM RF classifier, whereas, for the GLCM-13 RF classifier, an accuracy of 97.3 % was observed. Thus, overall, a 3.5 % improvement in accuracy was observed while using the GLCM RF classifier with window size 13. The textural information with proper calibration over high-spatial resolution datasets improves crop delineation in the present study. Henceforth, a more accurate cropland identification will provide a better estimate of the actual cropland area in such an arid region, which will assist in formulating a better resource management policy.

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Remote sensing datasets and methods are suitable for mapping and managing the natural resources like minerals, clean water, and energy and also govern their sustainability nowadays. Hyperspectral (HS) imaging has immense potential for rock type classification, mineral mapping, and identification. This work demonstrates the potential of feature extraction techniques and unsupervised machine learning methods for the space-borne hyperspectral remote sensing data in characterizing and identifying mineral and classifying rock type in Banswara, Rajasthan, India. Feature extraction techniques can reveal variations within the data, which can help identify geological areas, reduce noise, and check the dimensionality of the data. Singular value decomposition (SVD)-based principal component analysis (PCA), kernel PCA (KPCA), minimum noise fraction (MNF), and independent component analysis (ICA) were tested for lithological mapping using recently launched DLR Earth Sensing Imaging Spectrometer Hyperspectral (DESIS) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) data in order to map geologically significant areas. Unsupervised machine learning methods, such as Iterative Self-Organizing Data Analysis Technique (ISODATA) and K-means, were also employed. Vertex component analysis (VCA) was utilized to check for similarity and identify various spectral features. Our work demonstrates the advantages of using feature extraction algorithms such as PCA and KPCA over MNF and ICA in geological mapping and interpretability. We recommend K-means as the preferred method for lithological classification of hyperspectral remote sensing data. Our work highlights the potential of advanced feature extraction algorithms for mineral mapping using hyperspectral data, providing different ways to identify minerals and ultimately leading to better mineral resource management.

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Rapid urbanisation and industrialisation coupled with overpopulation have altered land cover/land use (LCLU) and surface temperature (ST) patterns in Dehradun. Monitoring these changes through satellite-based remote sensing is required to ensure the sustained development of this ecologically fragile region. Here, LU and ST dynamics of the Dehradun municipal area have been estimated using Landsat-5 datasets for 1991, 1998, and 2008 and Landsat-8 dataset for 2018. LU maps have been extracted using a Gaussian Maximum Likelihood classifier with an overall accuracy of over 88% and Kappa coefficients above 0.85. Results reveal that the urban region expanded by 80.6% in the 27 years while dense vegetation and dry river bed classes have declined sharply. Sparse vegetation has risen by 3 km2, whereas bare ground has decreased by about 4.3 km2. Mean ST has increased above 9 °C from 1991 to 2018 in every season. A seasonal influence is evident on the mean ST per LU class's trend, which rose between 8 °C and 12 °C for every LU class, indicating significant warming across each LU class. ST probably has non-linear relationships with its causal factors represented by spectral indices, elevation, and population density. Urban heat island (UHI) formation is thus evinced, promulgating the administration's urgent action to save the environment and redrawing policies for ambitious projects like smart cities.

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BACKGROUND: In Geographical Information Systems issues of scale are of an increasing interest in storing health data and using these in policy support. National and international policies on treating HIV (Human Immunodeficiency Virus) positive women in India are based on case counts at Voluntary Counseling and Testing Centers (VCTCs). In this study, carried out in the Indian state of Andhra Pradesh, these centers are located in subdistricts called mandals, serving for both registration and health facility policies. This study hypothesizes that people may move to a mandal different than their place of residence for being tested for reasons of stigma. Counts of a single mandal therefore may include cases from inside and outside a mandal. HIV counts were analyzed on the presence of outside cases and the most likely explanations for movement. Counts of women being tested on a practitioners' referral (REFs) and those directly walking-in at testing centers (DWs) were compared and with counts of pregnant women. RESULTS: At the mandal level incidence among REFs is on the average higher than among DWs. For both groups incidence is higher in the South-Eastern coastal zones, being an area with a dense highway network and active port business. A pattern on the incidence maps was statistically confirmed by a cluster analysis. A spatial regression analysis to explain the differences in incidence among pregnant women and REFs shows a negative relation with the number of facilities and a positive relation with the number of roads in a mandal. Differences in incidence among pregnant women and DWs are explained by the same variables, and by a negative relation with the number of neighboring mandals. Based on the assumption that pregnant women are tested in their home mandal, this provides a clear indication that women move for testing as well as clues for explanations why. CONCLUSIONS: The spatial analysis shows that women in India move towards a different mandal for getting tested on HIV. Given the scale of study and different types of movements involved, it is difficult to say where they move to and what the precise effect is on HIV registration. Better recording the addresses of tested women may help to relate HIV incidence to population present within a mandal. This in turn may lead to a better incidence count and therefore add to more reliable policy making, e.g. for locating or expanding health facilities.

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