Harsh Shinde

MRSAC | Research Intern

Harsh Shinde | Jan 2025 to Present

Technologies: Geospatial Technologies, Remote Sensing, Spatial Data Analysis, EO, GIS

Role: Analyzing land use/land cover change, urban heat islands, and land surface temperature (LST) variations in the Nagpur region using remote sensing data. Creating building footprint datasets for regions in Turkey through high-resolution satellite imagery and GIS techniques. Detecting farm boundaries using EO data and spatial analysis to support agricultural land mapping and monitoring.

Landslide Detection and Mapping Using Deep Learning Across Multi-Source Satellite Data and Geographic Regions

Rahul Burange, Harsh Shinde, Omkar Mutyalwar

TLDR: This study integrates deep learning with multi-source satellite data for accurate landslide detection across diverse terrains. It evaluates models like U-Net, DeepLabV3+, and ResNet using Sentinel-2 and ALOS PALSAR data to improve prediction and risk management.

A Comprehensive Approach to Landslide Detection: Deep Learning and Remote Sensing Integration

Rahul Burange, Harsh Shinde, Omkar Mutyalwar

TLDR: This paper presents an integrated approach to landslide detection by combining deep learning techniques with remote sensing data. It emphasizes improved accuracy through multi-source data fusion and advanced neural network models.

An Exhaustive Review on Deep Learning for Advanced Landslide Detection and Prediction from Multi-Source Satellite Imagery

Rahul Burange, Harsh Shinde, Omkar Mutyalwar

TLDR: This review explores deep learning methods for advanced landslide detection and prediction using diverse satellite imagery sources. It highlights model architectures, data fusion strategies, and challenges in real-world applications.

DeepSlide: Landslide Detection and Mapping Using Deep Learning Across Multi-Source Satellite Data and Geographic Regions

Rahul Burange, Harsh Shinde, Omkar Mutyalwar

Description: This study integrates deep learning with multi-source satellite data for accurate landslide detection across diverse terrains. It evaluates models like U-Net, DeepLabV3+, and ResNet using Sentinel-2 and ALOS PALSAR data to improve prediction and risk management.

Assessment of Urban Heat Island and Land Surface Temperature using Landsat 8 and 9 Satellite Imagery and GIS

Harsh Shinde

Description: Analysis of urban heat island effects and land surface temperature variations using advanced satellite imagery and Geographic Information Systems (GIS) techniques.

Satellite Imagery Semantic Segmentation

Harsh Shinde

Description: Implementation of semantic segmentation techniques for satellite imagery analysis using deep learning approaches.

Google Cloud Arcade Facilitator | Mentor

Harsh Shinde | July 2024 to Present

Role: Mentored and guided 600+ students in Google Cloud Platform (GCP), covering Kubernetes, Cloud Functions, Apigee, Firebase, and core services like compute, storage, and networking. Provided hands-on support with Kubernetes cluster management, serverless Cloud Functions, API management via Apigee, and Firebase integration for scalable applications. Organized events, solved technical doubts, and maintained student engagement through continuous support and motivation.

Google Summer of Code 2025 - OSGeo Foundation

Harsh Shinde

Project: Building tools that make geospatial datasets more AI-ready using standards like GeoCroissant and STAC. Contributing to the OSGeo Foundation as part of Google Summer of Code 2025, focusing on Earth observation, data-centric AI, and metadata standardization.

Second Runner-Up on VNIT Geo Spatial AI Challenge organized by ISRO, RRSC, Nagpur

Harsh Shinde, Aditya Singh, Ankit Kirtane

Details: The team secured Second Runner-Up position in the prestigious VNIT Geo Spatial AI Challenge organized by ISRO's RRSC, Nagpur, in August 2024. The trophy and certificates were awarded by Dr. G. Sreenivasan, General Manager of RRSC ISRO. The team was later felicitated at KDK College of Engineering by Principal Dr. V.P. Varghese, Director Dr. A.M. Badar, and Head of AI & DS Department Dr. S.M. Malode.

Project Details: Tree/Orchard Detection and Counting from High-Resolution Satellite Images," focused on detecting and counting individual trees or orchards in very high-resolution datasets using Machine Learning (ML) and Deep Learning (DL) techniques. The competition highlighted the commercial importance of accurately identifying horticultural plantations.

B.Tech in Electronics and Telecommunication Engineering

(2021 - 2025) | CGPA: 8.01/10

Karmaveer Dadasaheb Kannamwar Engineering College (KDKCE), Nagpur, India