Summary of Seefar: Satellite Agnostic Multi-resolution Dataset For Geospatial Foundation Models, by James Lowman et al.
SeeFar: Satellite Agnostic Multi-Resolution Dataset for Geospatial Foundation Models
by James Lowman, Kelly Liu Zheng, Roydon Fraser, Jesse Van Griensven The, Mojtaba Valipour
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents SeeFar, a dataset of multi-resolution satellite images from public and commercial satellites, designed to train geospatial foundation models. The dataset combines pre-processed images with varying spatial resolutions (30m to 1m) and spectral bands (Blue, Green, Red, and Near-Infrared), in cloud-optimized GeoTIFF format. The goal is to enable users to utilize historical data alongside higher-resolution imagery, offering greater flexibility during inference. SeeFar aims to bridge the gap between public and commercial satellite images by standardizing data from diverse sources, normalizing formats, and aligning spectral bands. This aggregation makes processed and consistent satellite data accessible to a wider range of users, fostering competition and innovation in satellite imagery analysis. The dataset is available at this URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SeeFar is a big collection of high-resolution pictures taken by satellites. These images can be used to train machines to recognize patterns in the Earth’s surface. The problem is that these images are often hard to get because they’re expensive or not publicly available. SeeFar solves this by combining different types of satellite images into one dataset, making it easier for people to use them. The dataset includes pictures with varying resolutions and colors, and also comes with extra information about each image. This makes it possible for researchers, policymakers, and others to analyze the data in a more consistent way. By providing access to this dataset, SeeFar aims to encourage new ideas and innovations in using satellite imagery. |
Keywords
» Artificial intelligence » Inference
