article.md

Problem statement

The objective of this task is to classify different landcover types in a satellite image. This problem is approached as a machine learning task known as semantic segmentation, where the goal is to predict the class label for each individual pixel in the image.

Dataset

The dataset used for this project is from the 2018 DeepGlobe Landcover Classification Challenge. It consists of a total of 803 satellite images, each with dimensions of 2448x2448 pixels. Each image in the dataset is accompanied by a segmentation mask that assigns class labels to the pixels.

Landcover Name	Color	Explanation / Function
Urban land	Cyan	Man-made, built-up areas with human artifacts
Agriculture land	Yellow	Farms, planned plantations, cropland, orchards
Rangeland	Magenta	Non-forest, non-farm, green land, grass
Forest land	Green	Land with at least 20% tree crown density and clear cuts
Water	Blue	Rivers, oceans, lakes, wetlands, ponds
Barren land	White	Mountains, rocks, deserts, beaches, vegetation-free land
Unknown	Black	Clouds and others

Model

For this task, we utilized a pre-trained UNet model with weights pretrained on the ImageNet dataset. We then fine-tuned the UNet using the DeepGlobe Landcover Classification dataset. The training process took approximately 2 hours using a single NVIDIA T4 GPU.

Team members

David Mora

Eduard's Mendez

Santiago Ahumada

Aditional information

If you are interested in contributing to the project or just getting more information about the details you can head over to our GitHub repository.