SeeFar is an evolving collection of multi-resolution satellite images from
public and commercial satellites. We specifically curated this dataset for
training geospatial foundation models, unconstrained by satellite type. In
recent years, advances in technology have made satellite imagery more
accessible than ever. More earth-observing satellites have been launched in the
last five years than in the previous fifty. Modern commercial satellites now
offer up to 100 times the spatial resolution of public access satellites.
However, the high cost and limited historical availability of commercial
satellite imagery is a barrier to the training of foundational models,
impacting what images can be used during inference. The SeeFar dataset
represents a step towards training models that are satellite-agnostic by
combining multi-resolution commercial and public access pre-processed images.
This will enable users to utilize historical data alongside higher-resolution,
more expensive satellite imagery, offering greater flexibility during
inference. To achieve this, we describe a process for standardizing data from
diverse satellite sources, normalizing different data formats, and aligning
spectral bands to enhance interoperability. The SeeFar dataset includes images
at a resolution of 384x384 pixels, spanning four spectral bands (Blue, Green,
Red, and Near-Infrared) and expanding spatial resolutions (starting with 30,
10, 1.5, and 1.0 meters), all in cloud-optimized GeoTIFF format. It also
provides consistent and comprehensive metadata to enhance data transparency and
reliability. By aggregating data from multiple sources, SeeFar makes processed
and consistent satellite data accessible to a wider range of users - from
researchers to policymakers - fostering competition and innovation in satellite
imagery analysis. The dataset is available at \url{coastalcarbon.ai/seefar}.

SeeFar 数据集是一个多分辨率卫星图像数据集，用于训练地理空间基础模型，解决商业卫星图像的高成本和有限历史可用性对基础模型训练的影响，并实现多源数据标准化和增强互操作性。