Satellite Image Feature Detection

The proliferation of satellite imagery has given us a radically improved understanding of our planet. It has enabled us to better achieve everything from mobilizing resources during disasters to monitoring effects of global warming. What is often taken for granted is that advancements such as these have relied on labeling features of significance like building footprints and roadways fully by hand or through imperfect semi-automated methods. This project report is an analysis and implementation of a kernel that was published for the DSTL Satellite Imagery Feature Detection challenge run by Kaggle . The approach is based on an adaptation of fully convolutional neural network for multispectral data processing using UNET and RESNET.

Paper Link : https://drive.google.com/file/d/1Hba-GvEJMNi7OWZbIdMb3AwUUNJLfjKq/view?usp=sharing

NoteBook

EDA : https://colab.research.google.com/drive/1GiAFQ9jlyYZ_X4sfFlhY23dUUI2MEZQf?usp=sharing

Unet : https://colab.research.google.com/drive/15AoIVS5sT5_4YEdcnMah4UcSYNffXD7k?usp=sharing