Space Apps Challenge 2017

Water Tracker | Where's the Water?

Silicon Valley (Palo Alto), CA

Awards & Nominations

Water Tracker has received the following awards and nominations. Way to go!

Local Peoples' Choice Winner
Global Nominee

The Challenge | Where's the Water?

Use satellite and other data to allow farmers, landowners, and land managers in your locale to identify and visualize water resources in their surroundings.

Water Tracker

Water Tracker is a neural network trained to detect water location using satellite imagery. It can be used to find water during a drought, to predict flooding, to research water movement over time, or in many other ways.

Water Tracker

Check out the live demo at http://tinyurl.com/WaterTrackerApp

Water Tracker will help monitor water-related ecosystems including wetlands, rivers, aquifers, and lakes. Any sudden or large changes in water quantity can be identified and a machine learning algorithm can be applied to correlate it with environmental conditions and find the potential causes.


Overview:

  • WaterTracker is a neural network to locate and track water (in near-real-time) usingsatellite imagery
  • It canbe used by farmers, scientists, researchers, and citizens through aneasy-to-use web-app
    • A possible use is finding water during a drought
    • Another potentialuse for this data is flood prediction by correlating water movement withelevation data
    • Also, it can be used to analyse water movement over time
  • Itis trained on known water locations andcrowdsourced data

Features:

  • A user can open the web-app and choose some options, then predict water locations using the neural network:
    • Precision–More precise calculations take longer, but can detect smaller bodies of water
    • Date–The system is able to use archived data to track changes in water over time
  • Itwill show the areas which are water and the areas which are (most likely) notwater
  • Thereis also an option to download the data for further analysis instead of viewingit on the map

Future Direction:

  • More training data for neural network – Using more training data (from existing topographical maps) would allow the neural network to be smarter and more accurate.
  • More types of datasets – The project could also be improved by using more datasets along with satellite data (i.e. LIDAR from satellites and other NASA data which is not publicly available)
  • Mobile App - A simple, easy-to-use mobile app for anybody to easily view water location data.

Data Used:

  • United States Geographical Survey Earth Explorer & Datasets
  • Sentinel-2 Satellite Imagery
  • NASA Shuttle Radar Topography Mission Water Data (Only for training data for neural network)

Technology Used:

  • Neural Network - Tensorflow Machine Learning library (with Python)
  • Web App - Node.js with HTML5 & JavaScript
    • Map Display - Leaflet.js
  • Data Storage - GeoTIFF & CSV files
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