HackBama Project-Firewatch | And YOU can Help Fight Fires!

Huntsville, AL

Awards & Nominations

HackBama Project-Firewatch has received the following awards and nominations. Way to go!

Global Nominee

The Challenge | And YOU can Help Fight Fires!

Build a fire-monitoring and crowdsourcing tool that will allow local fire managers to respond to wildfires.

Project Fire-watch

Project-Firewatch integrates crowd sourced, low cost hardware fire monitors with data from the Fire Information for Resource Management System (FIRMS) to provide users with granular, yet highly encompassing wildfire information that is easily accessible.

HackBama Project-Firewatch

Access the Project-Firewatch Wildfire Map Here!

Access the Project-Firewatch API Here!

Demo sites may take a moment to load, the server is not powerful and the project still needs a lot of optimization work.

Table of Contents

Quick start

To set up a project-firewatch server, do the following.

git clone https://github.com/osteth/project-firewatch.git

Then install in locally editable (-e) mode and run the tests.

pip install -e .[test]
    #tests aren't yet written and may fail.

Finally, give the command line program a try.

projectfirewatch --help

Command-line Interface

Project Firewatch Includes a Command-line Interface(CLI) to ensure easy use for non-technical personell as well as provide powerfull POSIX compliant features needed by experienced systems administrators and highly techincal persons.


projectfirewatch <commads><options><arguments>
projectfirewatch --help
projectfirewatch update
projectfirewatch start

CLI Development Roadmap

  • [x] Build Basic CLI functionality.
  • [x] Write a function to automatically download MODIS Data.
  • [x] Write a function to re-format MODIS data into a usable JSON format.
  • [x] Combine download and format function and add to CLI.
  • [x] Improve Error resilience of update function.
  • [x] Integrate API with CLI.
  • [x] Integrate Map with CLI.
  • [ ] Write unit tests.
  • [ ] add a periodic auto-update of MODIS data to server function.
  • [ ] Add Daemon functionality to CLI.
  • [ ] make GUI extension??


MODIS Satellite Data Explanation

Attribute Short Description Long Description
Latitude Latitude Center of 1km fire pixel but not necessarily the actual location of the fire as one or more fires can be detected within the 1km pixel.
Longitude Longitude Center of 1km fire pixel but not necessarily the actual location of the fire as one or more fires can be detected within the 1km pixel.
Brightness Brightness temperature 21(Kelvin) Channel 21/22 brightness temperature of the fire pixel measured in Kelvin.
Scan Along Scan pixel size The algorithm produces 1km fire pixels but MODIS pixels get bigger toward the edge of the scan. Scan and track reflect actual pixel size.
Track Along Track pixel size The algorithm produces 1km fire pixels but MODIS pixels get bigger toward the edge of the scan. Scan and track reflect actual pixel size.
Acq_Date Acquisition Date Date of MODIS acquisition.
Acq_Time Acquisition Time Time of acquisition/overpass of the satellite (in UTC).
Satellite Satellite A = Aqua and T = Terra.
Confidence Confidence (0-100%) This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence estimates range between 0 and 100% and are assigned one of the three fire classes (low-confidence fire, nominal-confidence fire, or high-confidence fire).
Version Version (Collection and source) Version identifies the collection (e.g. MODIS Collection 6) and source of data processing: Near Real-Time (NRT suffix added to collection) or Standard Processing (collection only). "6.0NRT" - Collection 6 NRT processing. "6.0" - Collection 6 Standard processing. Find out more on collections and on the differences between FIRMS data sourced from LANCE FIRMS and the University of Maryland.
Bright_T31 Brightness temperature 31 (Kelvin) Channel 31 brightness temperature of the fire pixel measured in Kelvin.
FRP Fire Radiative Power Depicts the pixel-integrated fire radiative power in MW (megawatts).
DayNight Day / Night D = Daytime, N = Nighttime


requests modifiers Long Description
lat plus minus /api/?lat=43.6271&plus=10&minus=10 results filteres to a latitude plus and minus a given location.
lon plus minus /api/?lat=43.6271&plus=10&minus=10 results filteres to a longitude plus and minus a given location.
scan-min /api/?scan-min=1
scan-max /api/?scan-max=2
track-min /api/?track-min=1
track-max /api/?track-max=2
date end /api/?date=<julian begin date>&end=<julian end date>
time until /api/?time=0255&until=2250 (in UTC and military time).
onland /api/?onland=True
confidence /api/?confidence=65 (sets minimum confidence of results)
frp /api/?frp=16 (sets minimum power of results in megawatts).
day /api/?onland=True

API Development Roadmap

  • [x] Get basic FlaskAPI functionality working.
  • [x] Get pretty FlaskAPI markdown displayer working.
  • [x] Figure out how to get MODIS data and.
  • [x] Pull in MODIS data from flat file.
  • [x] Allow the user to dump all MODIS data at request.
  • [ ] collaborate with data source provider to better offer data to the community.
  • [ ] Write Unit Tests
  • [ ] Switch over to DB from the flat file.
  • [ ] Create a specification for MODIS data queries.
  • [ ] Expand API to allow for data filtering.
  • [ ] Create Specification for Sensor Data.
  • [ ] Expand API to allow writing of sensor data to DB.
  • [ ] Expand API to allow querying of sensor data.


The map displays all known satellite discovered wildfires as well as the location and status of all active sensors.

Map Limitations

Due to technical Limitations, we are attempting to overcome the map can only display a maximum of 3240 points. To mitigate the effects of this limitation, we use GeoIP information to automatically locate the user and only plot fires within 25 degrees in all directions of the user. Additionally, a green marker is dropped were the program believes the user is located and the map automatically focuses on the user's location.

Map Features

Displays Semi-Realtime MODIS Satellite gathered wildfire Information.


Displays Status and information about custom fire sensors.


Satellite Information allows the user to get a broad picture of where the fire is located.


Project Firewatch located user's location and automatically focus the map for ease of use.


showing 25-degree bounding box size.


Map Development Roadmap

  • [x] Basic Map Functionality.
  • [x] Display a pin and circle.
  • [x] figure out radius expansion algorithm to accurately display MODIS satellite data.
  • [x] Display mock sensor data.
  • [x] Integrate with CLI.
  • [x] Get coordinates of user and autofocus map accordingly.
  • [ ] Get Map to display all MODIS fire Data.
  • [ ] Migrate from flat file to DB.
  • [ ] Display all live sensor data.
  • [ ] Allow the user to specify a focus location.
  • [ ] Attempt to pull cell phone GPS location and use it as users location.


We put together prototype hardware sensors for the competition that anyone can build and contribute crowdsourced Fire data back to our database via the Project-Firewatch API.




Bill of Materials

Orange pi zero - $6.99

4GB SD Card (class 10 speeds are needed for OS operability) - $2.49

AC-DC converter/ Power Regulator - $3.00

Plug-in Enclosure - $5.17

Keyes Flame Detection Sensor Module for Arduino - $2.66

KEYES DHT11 Digital Temperature Humidity Sensor Module for Arduino - $1.59

Total prototype parts cost: $21.90

Operating System

for the prototype sensors, we utilized the ARMBIAN Orange Pi Zero build available Here

Sensor Development Roadmap

  • [x] Build microprocessor version using low cost readily available modules.
  • [x] Get basic computing functions working.
  • [ ] Connect sensors and get sensor data.
  • [ ] Figure out how to send sensor data to API.
  • [ ] Write the client-side application to gather and send data to API.
  • [ ] Refactor to lower cost micro-controller (possibly Arduino nano).
  • [ ] write microcontroller firmware.
  • [ ] Implement a battery.
  • [ ] Design custom PCB.
  • [ ] V1 sensor test build.
  • [ ] V1 user trials.
  • [ ] Design Enclosure.
  • [ ] Enclosure fittest.
  • [ ] Develop automated testing fixturing and software for manufacturing.
  • [ ] Enumerate Q/A standards.
  • [ ] Get contract manufacturing quotes.
  • [ ] Deliver Build Documents and equipment to Contract Manufacturer.
  • [ ] Develop packaging and product ancillaries.
  • [ ] Product launch, sales, and delivery.

SpaceApps is a NASA incubator innovation program.