claramartiny/droughtwatch

Predict drought conditions in Northern Kenya using deep learning models (CNN) on satellite images. # Data analysis - Description: This project leverages deep learning and computer vision for drought resilience, using satellite images and human expert labels to detect drought conditions in Northern Kenya. - Data Source: USGS Landsat 8 Collection 1 Tier 1 and Real-Time data Raw Scenes - Type of analysis: - Dataset: The current dataset consists of 86,317 train and 10,778 validation satellite images, 65x65 pixels each, in 10 spectrum bands, with 10,774 images withheld to test long-term generalization (107,869 total). Human experts (pastoralists) have labeled these with the number of cows that the geographic location at the center of the image could support (0, 1, 2, or 3+ cows).Each pixel represents a 30 meter square, so the images at full size are 1.95 kilometers across. Pastoralists are asked to rate the quality of the area within 20 meters of where they are standing, which corresponds to an area slightly larger a single pixel. Since forage quality is correlated across space, the larger image may be useful for prediction. The data is in TFRecords format, split into train and val, and takes up 4.3GB (2.15GB zipped). You can learn more about the format of the satellite images here: developers.google.com # Startup the project The initial setup. Create virtualenv and install the project: ```bash $ sudo apt-get install virtualenv python-pip python-dev $ deactivate; virtualenv ~/venv ; source ~/venv/bin/activate ;\ pip install pip -U; pip install -r requirements.txt ``` Unittest test: ```bash $ make clean install test ``` Check for droughtwatch in gitlab.com. If your project is not set please add it: - Create a new project on `gitlab.com` - Then populate it: ```bash $ ## e.g. if group is "{group}" and project_name is "droughtwatch" $ git remote add origin git@gitlab.com:{group}/droughtwatch.git $ git push -u origin master $ git push -u origin --tags ``` Functionnal te …