Hazard Busters| Automated Detection of Hazards

Awards & Nominations

Hazard Busters has received the following awards and nominations. Way to go!

Global Nominee

Automated Detection of Hazards

Countless phenomena such as floods, fires, and algae blooms routinely impact ecosystems, economies, and human safety. Your challenge is to use satellite data to create a machine learning model that detects a specific phenomenon and build an interface that not only displays the detected phenomenon, but also layers it alongside ancillary data to help researchers and decision-makers better understand its impacts and scope.

Automated Hazard Detection

Summary

Making the best use of NASA satellite’s data, our team implemented a solution which uses CNN deep learning model to detect or predict potential natural hazards. At first, the model will be trained with already existing satellite images and the prediction output will be stored in a database that will be synchronized with the frontend web application. This is an interactive app which pushes notifications (to the areas they are interested in) to researchers, insurance companies, landowners and other parties, allowing them to make decisions about potential hazards.

How We Addressed This Challenge
  • Researched the favorable conditions for hazards and downloaded related satellite data
  • Created CNN Deep Learning Model
  • Trained the model with satellite images
  • Validated the output against the past fire prone areas to check the accuracy and tuned the parameters accordingly.
  • Stored the output as GeoJSON format in a database accessible by the web application.
  • Wireframed and built a Web and Mobile App with Ionic Framework that allows users to interact, modify, dismiss or alert about a fire outbreak
How We Developed This Project

Used a deep learning model with an object detection algorithm to find out a spot in the satellite data. Moreover we use computer vision and the OpenCV libraries for satellite image processing.

Once the output is generated, the data is stored as GeoJSON in the Firebase database so that any update is centrally triggered in real-time in Firebase. So that our server-less code can handle this with custom functions and notify the clients via push notification that have the mobile app installed. For the learning model was used Python FLASK, Google Collab, TensorFlow, GDAL and so on.


For the current project, the consumer application (Hybrid Mobile App), were chosen the following technologies:


  • React.js: Frontend JavaScript Framework
  • Ionic Framework, allowing the same source code being served as a Web Application or deployed to the Google Play Store or to Apple App Store. 
  • Google Firebase (Server-less) with real-time database and push notifications.
  • Google Maps API, although we will be moving to an OpenStreetMap server to save costs in the near future.
  • As the data communication format we have chosen GeoJSON.
  • The user is able to authenticate either with username/password or with Google.
How We Used Space Agency Data in This Project
  • The Landsat-8 and Modis data were used to detect vegetation (boreal forest), moisture content, Landuse/landcover, Fire region using indices like NDVI, NDMI, SPI, NBR. 
  • We combined Landsat-8 data along with CNES and CSA datasets.
  • With Aster DEM we obtained slope and elevation information.
  • Using the GEOS 16&17 satellite the abnormal change in the temperature near the burning region was identified to detect the source of fire within a few minutes of its outbreak.


In the future, We plan to add even social indicators which help us to better estimate damage and losses that will be incurred. When extending to detect other hazards we plan to include some more satellite images which helps us in predicting that particular hazard accurately.

Project Demo
  • To demonstrate our application, the frontend and backend will coordinate with each other. In the frontend, the user will login in the app and select a certain region he wants to monitor. Then it will send the coordinates to the backend so that the analysis can take place and the model will try to find the hazard within that region or in the configured radio.
  • Currently the application is in development state, however, some of the source code of the training model and the AI side can be found in the github repository.
  • The available implementation of the frontend application can be also found in github, and you can take a look at the current available version with dummy data online, but this app is being changed currently: https://vbarzana.github.io/hazard-busters 
  • Please see the detailed PowerPoint presentation with specific pictures, wireframe and other details here: https://docs.google.com/presentation/d/1TleRe3S3Pc545lq1g82-bnOumf26jqF67_92lfjc4ZY/edit?usp=sharing
Project Code
Data & Resources
Tags
#artificial intelligence, #hazards detection, #fire detection, #satellite data, #mobile application
Judging
This project was submitted for consideration during the Space Apps Judging process.