Fine tuning CLIP with Remote Sensing (Satellite) images and captions

Unlocking the potential of vision language models on satellite imagery through fine-tuning

RRS-10K: A Multitask Vision-Language Model Benchmark for Rare Remote Sensing Image Interpretation

arXiv:2607. 24810v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved strong performance on general remote sensing tasks.

RSGPNet: Geometric Prompting for Remote Sensing Open-Vocabulary Semantic Segmentation

arXiv:2606. 28410v1 Announce Type: cross Abstract: Open-vocabulary semantic segmentation (OVSS) enables text-guided segmentation of unseen objects, breaking fixed-class limitations to achieve open-world understanding.

Semi-Supervised Adaptation of Vision-Language Models for Image Classification

The paper introduces Self‑Evolutionary CLIP (SE‑CLIP), a semi‑supervised framework that adapts vision‑language models like CLIP to satellite imagery. SE‑CLIP uses a two‑phase pipeline: an initial warm‑up on a small set of annotated seeds followed by a recursive discovery phase that iteratively selects high‑confidence samples from unlabeled data. A class‑balanced selection strategy is applied to keep the evolving support set balanced, and experiments on the UCM and NWPU benchmarks show that SE‑CLIP outperforms existing semi‑supervised methods.

JL1-CC&QA: Extending the JL1-CD Benchmark with Change Captioning and Question Answering

arXiv:2606. 31745v1 Announce Type: cross Abstract: Remote sensing change detection (CD) traditionally focuses on pixel-level binary segmentation, which identifies where changes occur but neither what nor why.

Fine-grained CLIP fine-tuning with self-annotated region alignment

Contrastive Language-Image Pre-training (CLIP) has been shown to have limitations in its fine-grained dense feature representation, due to its pre-training focusing on matching the whole image to a text description. Considering the large data and computational burden in pre-training a vision-language model from scratch, a series of works aim to enhance the fine-grained ability of CLIP through a fine-tuning scheme.

Benchmarking Geospatial Foundation Models for Agriculture Applications

Geospatial foundation models pretrained on satellite imagery promise broad generalization across remote sensing tasks and regions, but their geographic transferability has not been systematically tested, especially in agriculture applications. This paper presents a controlled benchmark that evaluates three models, Prithvi, SpectralGPT, and SatMAE, on multi-temporal crop segmentation and change detection across four U.

ORBIT++: Benchmarking SfM in the Wild with 360{\deg} Video

arXiv:2608.22039v1 Announce Type: new Abstract: Structure-from-Motion (SfM) is a cornerstone of 3D perception, yet current methods often fail when applied to complex videos involving challenging came...

PhenoStitch: Training-Free Panoptic Crop Mapping from Satellite Image Time Series

arXiv:2608. 00870v1 Announce Type: cross Abstract: Panoptic crop mapping requires both delineating individual agricultural parcels and assigning a crop type to each parcel from satellite image time series.

LaVIDE: Language-Prompted Satellite Change Detection via Map-Image Alignment

arXiv:2411. 19758v2 Announce Type: replace-cross Abstract: Remote sensing change detection based on a map reference and an up-to-date image boosts timely observation of the Earth's surface when earlier images are lacking for comparison.

KALE: Kernel Alignment with Loss Equilibration for Stable CLIP-DINOv2 Alignment at Web Scale

arXiv:2607. 18885v1 Announce Type: new Abstract: Kernel-based alignment of CLIP toward a vision centric teacher such as DINOv2 (KUEA) improves CLIP's visual representations while preserving text-encoder compatibility, using a fixed trade-off weight tuned on curated ImageNet-1K.

Benchmarking Geospatial Foundation Models for Agriculture Applications

arXiv:2606. 29664v1 Announce Type: cross Abstract: Geospatial foundation models pretrained on satellite imagery promise broad generalization across remote sensing tasks and regions, but their geographic transferability has not been systematically tested, especially in agriculture applications.