Dr. Shailesh Deshpande Profile

Dr. Shailesh Deshpande

at Tata Consultancy Services Ltd.

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Publications (6)

Proceedings Article | 13 November 2024 Presentation + Paper

An efficient technique for hazard susceptibility mapping using AHP- and ML-based models

Shreyansh Aswal, Chaman Banolia, Shailesh Deshpande

Proceedings Volume 13197, 131970Q (2024) https://doi.org/10.1117/12.3031770

KEYWORDS: Machine learning, Data modeling, Landslides, Performance modeling, Raster graphics, Education and training, Landsat, Chromium, Roads, Rain

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Landslides are one of the most frequently occurring natural disasters worldwide, often resulting in significant casualties, substantial loss of life, and extensive property damage. Certain areas are more susceptible to landslides due to various natural factors such as soil type, average rainfall, and land cover, as well as human factors like proximity to roads, mining activities, and other human settlements. Over the years, researchers have employed various methods to identify areas with a higher likelihood of landslides. One such method is the Analytical Hierarchy Process (AHP), a multicriteria decision-making process. The main advantage of AHP is its welldefined mathematical framework, which incorporates expert opinions to generate insights. Additionally, various machine learning models have been used to calculate the susceptibility of an area to landslides, with their performance depending on the number and quality of training samples available. Generating input data for both AHP and machine learning models is time- and effort-intensive. It usually requires obtaining data from multiple sources at different resolutions, resampling to a common resolution, and mosaicking the data to form a unified raster for each input feature. Furthermore, all input features must be resampled to a common resolution. In our current work, we propose a unique technique that combines both AHP and machine learning-based models. Our primary objective is to accurately detect past landslides in a given region while optimizing the time and effort required to generate input data. We evaluate our proposed model’s performance in terms of accuracy on landslide data from Italy and compare it with the performance of standalone AHP and machine learning models in terms of (i) accuracy and (ii) the time and effort required to generate input data for each model. The results are promising, with our proposed model surpassing the AHP model’s performance and being almost as accurate as the standalone machine learning model, while requiring less than half the time and effort to generate input data compared to the standalone AHP and machine learning models.

Proceedings Article | 1 November 2024 Presentation + Paper

Assessing the impact of prolonged air pollution exposure on COVID-19 mortality in Maharashtra, India using remotely sensed data

Shreyansh Aswal, Chaman Banolia, Shailesh Deshpande

Proceedings Volume 13198, 131980C (2024) https://doi.org/10.1117/12.3031773

KEYWORDS: COVID 19, Artificial intelligence, Pollution, Air contamination, Carbon monoxide, Particles, Correlation coefficients, Random forests, Linear regression, Diseases and disorders

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Proceedings Article | 19 October 2023 Presentation + Paper

The effect of long-term exposure to air pollution on COVID-19 mortality in Western Maharashtra, India: a study using remotely sensed data

Shailesh Deshpande, Chaman Banolia, Shreyansh Aswal, Balamuralidhar P.

Proceedings Volume 12735, 127350E (2023) https://doi.org/10.1117/12.2680329

KEYWORDS: Pollution, COVID 19, Air contamination, Artificial intelligence, Random forests, Carbon monoxide, Sulfur, Nitrogen dioxide, Lawrencium, Ozone

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Proceedings Article | 26 October 2022 Paper

Industrial/metal roof detection from hyperspectral image in an urban scene

Chaman Banolia, Shailesh Deshpande, Balamuralidhar P.

Proceedings Volume 12269, 122690A (2022) https://doi.org/10.1117/12.2648513

KEYWORDS: Reflectivity, Metals, Hyperspectral imaging, Remote sensing, Visualization, Vegetation, Visible radiation, Spatial resolution

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Proceedings Article | 20 September 2020 Presentation + Paper

Leveraging spatial structure with CapsuleNet for identification of the land use classes

Shailesh Deshpande, Rohit Thakur, Balamuralidhar P.

Proceedings Volume 11535, 115350C (2020) https://doi.org/10.1117/12.2573980

KEYWORDS: Vegetation, Image processing, Neural networks

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Proceedings Article | 29 August 2016 Paper

Assessment of anticipated runoff because of impervious surface increase in Pune Urban Catchments, India: a remote sensing approach

Piyush Yadav, Shailesh Deshpande

Proceedings Volume 10033, 100333T (2016) https://doi.org/10.1117/12.2245397

KEYWORDS: Remote sensing, Earth observing sensors, Landsat, Sensors, Spatial resolution, Image classification, Information technology, Agriculture, Feature extraction, Stanford Linear Collider

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Showing 5 of 6 publications

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