EarthWise - Spot the Fire V3.0

A project for NASA International Space Apps Challenge - Spot the Fire

The 2020 NASA Space Apps Challenge that was selected by this group is called 'Spot the Fire V3.0'. This challenge required us to "propose, prototype, and present innovative ideas that use novel machine learning, data science, and data fusion for the prediction, detection and impact analysis of wildfires". Resources were available for this challenge. Among those resources, we decided to pick the MODIS Dataset from the NASA EarthData.

MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (originally known as EOS AM-1) and Aqua (originally known as EOS PM-1) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.MODIS standard quality Thermal Anomalies / Fire locations processed by the University of Maryland with a 3-month lag and distributed by FIRMS. These standard data (MCD14ML) replace the NRT (MCD14DL) files when available.

The dataset taken is a csv file format and has following the attributes (columns):

1. Latitude
2. Longitude
3. Brightness
4. Scan
5. Track
6. Acq_date
7. Acq_time
8. Satellite
9. Confidence
10. Version
11. Bright_t31
12. Frp
13. Daynight

Code utilised by this project is attached. Points accomplished in the code are explained below:

1. Plot histogram using brightness

2. Find quantile of brightness, latitude and longitude

3. Divide brightness in low, high and extreme on the basis of value and add this as a column

4. Categorize area on the basis of latitude and longitude

5. Preprocess the data by replacing object value with integer value

6. Apply different machine learning algorithms

   6.1. K - Neighbours

   6.2. SVC

   6.3. Random Forest

   6.4. Logistic Regression

   6.5. SGD