| ## **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](https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-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 | <span style="color:cyan">Cyan</span> | Man-made, built-up areas with human artifacts | | |
| | Agriculture land | <span style="color:yellow">Yellow</span> | Farms, planned plantations, cropland, orchards | | |
| | Rangeland | <span style="color:magenta">Magenta</span> | Non-forest, non-farm, green land, grass | | |
| | Forest land | <span style="color:green">Green</span> | Land with at least 20% tree crown density and clear cuts | | |
| | Water | <span style="color:blue">Blue</span> | Rivers, oceans, lakes, wetlands, ponds | | |
| | Barren land | <span style="color:gray">White</span> | Mountains, rocks, deserts, beaches, vegetation-free land | | |
| | Unknown | <span style="color:black">Black</span> | 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](https://github.com/DavidFM43/landcover-segmentation). | |