Europa: Increasing Accessibility of Geospatial Datasets

Nov 05, 2023

Abstract

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.

GeospatialDatasetsIGARSS 2022

Contributed by

Hal Shin , Natanael Exe , Ujwal Dutta , Tanka Raj Joshi , Sagar Verma , Siddharth Gupta

Related Research

Synthetix: Pipeline for Synthetic Geospatial Data Generation

Remote sensing is crucial in various domains, such as agriculture, urban planning, environmental monitoring, and disaster management. However, acquiring real-world remote sensing data can be challenging due to cost, logistical constraints, and privacy concerns. To overcome these limitations, synthetic data has emerged as a promising approach. We present an overview of the use of synthetic data for remote sensing applications.In this regard, we address three conditions that can drastically affect the optimization of computer vision algorithms: lighting conditions, fidelity of the 3D model, and resolution of the synthetic imagery data. We propose a highly configurable pipeline called Synthetix as part of our GeoEngine platform for synthetic data generation. Synthetix allows us to quickly create large amounts of aerial and satellite imagery under varying conditions, given a few samples of 3D objects on real-world scenes. We demonstrate our pipeline’s effectiveness by generating 3D scenes from 35 real-world locations and utilizing these scenes to generate different versions of datasets and answer the three questions. We conduct an in-depth ablation study and show that considering different environments and weather conditions increases the reliability and robustness of the deep learning networks.

02 January 2024

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.

09 November 2023