Code and resources from Team Kyogre's winning participation in Smart India Hackathon, 2022 - Software Edition.

Link to Prototype Demo Video.

Link to Project Documentation and Related Resources.

The identification of non-residential built-up areas and detecting clusters of such regions can greatly aid strategic industrial expansions, developmental planning, and understanding the earth’s topography in general.

• Landcover Visualization Frontend.
• Landcover Visualization Backend.
• Real-time Satellite Image Acquisition.
• Machine Learning Experiments for Landcover Type Detection.
• Application Pipelineing Utilities.

The proposed solution aims to achieve detection of non-residential built-up areas clusters through AI-driven analysis of Medium Resolution Satellite Imagery. In a systematic approach, the solution aims to,

• Prepare annotated datasets for using standard satellite imagery collected by NASA, ESA and ISRO.
• Utilize existing annotated datasets to support model building.
• Deep CNNs to detect, segment and identify clusters of non-residential built-up regions.
• Evaluate its performance on highly-populated regions like Mumbai, Kolkata, Bangalore, and Delhi that pose a complex topography to detect patterns from.
• Leverage geographic metadata and topological constraints to refine the solution formulation at multiple steps, including annotated dataset preparation and post-processing the Deep CNN results.
• As a user endpoint, we build a GUI tool that visualizes landcover segmentation overlays on geographic maps generated through a novel patchify-process-reconstruct pipeline.

Link to Project Presentation Video.

Link to Project Documentation and Related Resources.

• Image Preprocessing Algorithms for Enhancement and Color Restoration.
• Video Frame Processing Utilities.
• Deep Learning for Underwater Species Classification.

• High-definition underwater cameras are deployed using buoys, moored in the sea at an optimal distance from the coast.
• To detect, localize, and identify lethal marine species, a pattern recognition algorithm analyzes real-time optical image feed from high-definition underwater cameras.
• On detecting a potentially lethal threat approaching the coast, the system sounds a public alarm as well as a personalized alert on swimmers' wearable devices to warn them of a possible threat.