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@SIH-22-Kyogre

Team Kyogre at Smart India Hackathon 2022

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

Team Kyogre at Smart India Hackathon '22

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

Project 1 - SatVision: AI-Driven Detection of Non-Residential Built-Up Clusters from Satellite Images

Link to Prototype Demo Video.

Link to Project Documentation and Related Resources.

satvision-conceptual-setup

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.

Repositories

Specific Objectives

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.

Project 2 - EyeSea: Real-time Automatic Marine Species Threat Alerting at Shoreline via Underwater Surveillance

Link to Project Presentation Video.

Link to Project Documentation and Related Resources.

eyesea-conceptual-setup

Repositories

Specific Objectives

  • 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.

Pinned Loading

  1. EyeSea_Image-Preprocessing-Algorithms Public

    Callable image enhancement and restoration APIs in Python. Preprocessing experiments and applicators for EyeSea.

    Python 1

  2. EyeSea_Video-Frame-Processing Public

    Real-time video frames depduplication and processing algorithm for frame-wise image processing.

    Python

  3. EyeSea_DL-for-Species-Classification Public

    Deep learning models for classification of underwater species from underwater images.

    Jupyter Notebook

  4. SatVision_Satellite-Image-Acquisition Public

    Pluggable interfaces and session management wrappers for Sentinel-Hub. Experiments, workflows, and tests to acquire a real-time feed of medium resolution satellite images from any Sentinel-Hub -sup…

    Python 1

Repositories

Showing 10 of 10 repositories
  • SatVision_ML-Pipelining-Utils Public

    Utilities to prepare the pipeline elements that integrate into SatVision: deep learning model inference, preprocessing, data acquisition, and post processing.

    Python 0 0 0 0 Updated Sep 6, 2023
  • EyeSea_Image-Preprocessing-Algorithms Public

    Callable image enhancement and restoration APIs in Python. Preprocessing experiments and applicators for EyeSea.

    Python 0 1 0 0 Updated Sep 6, 2023
  • .github Public
    0 0 0 0 Updated May 19, 2023
  • JavaScript 0 0 0 0 Updated May 19, 2023
  • SatVision_MapViewer-Backend Public

    Backend workflows for SatVision's real-time satellite image and land cover segmentation renderer. Deploys the novel patchify-process-reconstruct approach to run DL-driven segmentation inference in real-time.

    Python 0 0 0 0 Updated May 19, 2023
  • SatVision_Satellite-Image-Acquisition Public

    Pluggable interfaces and session management wrappers for Sentinel-Hub. Experiments, workflows, and tests to acquire a real-time feed of medium resolution satellite images from any Sentinel-Hub -supported satellites. Sentinel-2 (ESA), Landsat-8 (NASA), and Resourcesat (ISRO) satellites, etc.

    Python 1 0 0 0 Updated May 19, 2023
  • EyeSea_Video-Frame-Processing Public

    Real-time video frames depduplication and processing algorithm for frame-wise image processing.

    Python 0 0 0 0 Updated Aug 29, 2022
  • EyeSea_DL-for-Species-Classification Public

    Deep learning models for classification of underwater species from underwater images.

    Jupyter Notebook 0 0 0 0 Updated Aug 29, 2022
  • SatVision_Land-Cover-Detection Public

    Deep learning for land cover detection from medium resolution satellite imagery

    Jupyter Notebook 0 0 0 0 Updated Aug 29, 2022
  • EyeSea_Awesome-Underwater-Datasets Public Forked from xahidbuffon/Awesome_Underwater_Datasets

    Pointers to large-scale underwater datasets and relevant resources.

    0 126 0 0 Updated Mar 9, 2022