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Found 4 papers, 3 papers with code
TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning
To fill this gap, we propose TorchSpatial, a learning framework and benchmark for location (point) encoding, which is one of the most fundamental data types of spatial representation learning.
Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation
Geolocating precise locations from images presents a challenging problem in computer vision and information retrieval. Traditional methods typically employ either classification, which dividing the Earth surface into grid cells and classifying images accordingly, or retrieval, which identifying locations by matching images with a database of image-location pairs.
On the Promises and Challenges of Multimodal Foundation Models for Geographical, Environmental, Agricultural, and Urban Planning Applications
The advent of large language models (LLMs) has heightened interest in their potential for multimodal applications that integrate language and vision.
Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models
As image databases grow each year, performing automatic segmentation with deep learning models has gradually become the standard approach for processing the data.
