Kaggle Competition - https://www.kaggle.com/c/planet-understanding-the-amazon-from-space

Every minute, the world loses an area of forest the size of 48 football fields. And deforestation in the Amazon Basin accounts for the largest share, contributing to reduced biodiversity, habitat loss, climate change, and other devastating effects. But better data about the location of deforestation and human encroachment on forests can help governments and local stakeholders respond more quickly and effectively.

This goal of this competition is to label satellite image chips with atmospheric conditions and various classes of land cover/land use. Resulting algorithms will help the global community better understand where, how, and why deforestation happens all over the world - and ultimately how to respond.

This dataset contains 40,479 (RGB) satellite images, along with their associated labels.

Due to computation limits, only 5,365 images were used for this project.

The training set contains 3,755 satellite images, accounting for 70% of the total images.

The validation set contains 1,610 satellite images, the remaining 30% of the images.

• A custom, sequential CNN model was built which included convolution, max pooling, dropout, and dense layers.
• There are ~4 million parameters in this model.

• A pre-trained ResNet50 model trained on the ImageNet dataset.
• Fine-tuned using transfer learning

To train the models yourself, open and run the Report.ipynb notebook.

Results can also be viewed on Google Colab