Image retrieval enables an efficient search through vast amounts of satellite
imagery and returns similar images to a query. Deep learning models can
identify images across various semantic concepts without the need for
annotations. This work proposes to use Geospatial Foundation Models, like
Prithvi, for remote sensing image retrieval with multiple benefits: i) the
models encode multi-spectral satellite data and ii) generalize without further
fine-tuning. We introduce two datasets to the retrieval task and observe a
strong performance: Prithvi processes six bands and achieves a mean Average
Precision of 97.62\% on BigEarthNet-43 and 44.51\% on ForestNet-12,
outperforming other RGB-based models. Further, we evaluate three compression
methods with binarized embeddings balancing retrieval speed and accuracy. They
match the retrieval speed of much shorter hash codes while maintaining the same
accuracy as floating-point embeddings but with a 32-fold compression. The code
is available at this https URL

使用地理空间基础模型 Prithvi 进行遥感图像检索，在多光谱卫星数据上取得了出色的性能，提出了三种压缩方法，能够在保持浮点数嵌入精度的同时实现 32 倍的压缩比，而且与更短哈希码的检索速度相匹配。

利用地理空间基础模型的多光谱遥感图像检索

Multi-Spectral Remote Sensing Image Retrieval Using Geospatial  Foundation Models

Individual trajectories, containing substantial information on
human-environment interactions across space and time, is a crucial input for
geospatial foundation models (GeoFMs). However, existing attempts, leveraging
trajectory data for various applications have overlooked the implicit
spatial-temporal dependency within trajectories and failed to encode and
represent it in a format friendly to deep learning, posing a challenge in
obtaining general-purpose trajectory representations. Therefore, this paper
proposes a spatial-temporal joint representation learning method (ST-GraphRL)
to formalize learnable spatial-temporal dependencies into trajectory
representations. The proposed ST-GraphRL consists of three compositions: (i) a
weighted directed spatial-temporal graph to explicitly construct mobility
interactions over both space and time dimensions; (ii) a two-stage jointly
encoder (i.e., decoupling and fusion) to learn entangled spatial-temporal
dependencies by independently decomposing and jointly aggregating space and
time information; (iii) a decoder guides ST-GraphRL to learn explicit mobility
regularities by simulating the spatial-temporal distributions of trajectories.
Tested on three real-world human mobility datasets, the proposed ST-GraphRL
outperformed all the baseline models in predicting movement spatial-temporal
distributions and preserving trajectory similarity with high spatial-temporal
correlations. We also explore how spatial-temporal features presented in latent
space, validating that ST-GraphRL understands spatial-temporal patterns. This
method is also transferable for general-purpose geospatial data representations
for broad downstream tasks, as well advancing GeoFMs developing.

本研究提出了一种空间 - 时间联合表示学习方法 (ST-GraphRL)，用于将可学习的空间 - 时间依赖性编码到轨迹表示中。该方法在预测移动空间 - 时间分布和保持轨迹相似性方面在三个真实世界人类移动数据集上优于所有基准模型，并能理解潜在空间 - 时间模式。同时，该方法在广泛的地理空间数据表示和 GeoFMs 开发方面具有可迁移性。