Europa: Increasing Accessibility of Geospatial Datasets
Nov 05, 2023
In this paper we introduce a novel platform for teams to develop rich, analysis-ready datasets for geospatial machine learning. Europa 1 1 https://europa.granular.ai addresses longstanding challenges that remote sensing and machine vision researchers face when developing datasets, including data sourcing, dataset development and sharing. By simplifying and accelerating the dataset creation process, Europa serves to expedite the pace of geospatial machine learning innovation. The platform enables users to develop feature-rich, spatio-temporal datasets using multiple sources of satellite imagery. Europa supports the development of datasets for segmentation, classification, object detection, and change detection problems. Europa also enables collaborative dataset development, with a management protocol for crowdsourcing labels and annotations. The web interface and API are built upon a resilient dataset management protocol that supports versioning, forking and access control, enabling greater research collaboration.
Hal Shin , Natanael Exe , Ujwal Dutta , Tanka Raj Joshi , Sagar Verma , Siddharth Gupta
Post Wildfire Burnt-up Detection using Siamese UNet
In this article, we present an approach for detecting burnt area due to wild fire in Sentinel-2 images by leveraging the power of Siamese neural networks. By employing a Siamese network, we are able to efficiently encode the feature extraction process for pairs of images. This is achieved by utilizing two branches within the Siamese network, which capture and combine information at different resolutions to make predictions. The weights are shared between these two branches in siamese networks. This design allows to effectively analyze the changes between two remote sensing images, enabling precise identification of areas impacted by forest wildfires in the state of California as part of ChaBuD challenge thereby assisting local authorities in effectively monitoring the impacted regions and facilitating the restoration process. We experimented with various model architectures to train ChaBuD dataset and carefully evaluated the performance. Through rigorous testing and analysis, we have achieved promising results, ultimately obtaining a final private score (IoU) of 0.7495 on the hidden test dataset. The code is available at https://github.com/kavyagupta/chabud. We also deploy the final model as a point solution for anyone to use at https://firemap.io.
Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data
The advent of multitemporal high resolution data, like the Copernicus Sentinel-2, has enhanced significantly the potential of monitoring the earth's surface and environmental dynamics. In this paper, we present a novel deep learning framework for urban change detection which combines state-of-the-art fully convolutional networks (similar to U-Net) for feature representation and powerful recurrent networks (such as LSTMs) for temporal modeling. We report our results on the recently publicly available bi-temporal Onera Satellite Change Detection (OSCD) Sentinel-2 dataset, enhancing the temporal information with additional images of the same region on different dates. Moreover, we evaluate the performance of the recurrent networks as well as the use of the additional dates on the unseen test-set using an ensemble cross-validation strategy. All the developed models during the validation phase have scored an overall accuracy of more than 95%, while the use of LSTMs and further temporal information, boost the F1 rate of the change class by an additional 1.5%.