Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery
Unsupervised pre-training methods for large vision models have shown to enhance
performance on downstream supervised tasks. Develo** similar techniques for satellite …
performance on downstream supervised tasks. Develo** similar techniques for satellite …
A review of explainable AI in the satellite data, deep machine learning, and human poverty domain
Recent advances in artificial intelligence and deep machine learning have created a step
change in how to measure human development indicators, in particular asset-based …
change in how to measure human development indicators, in particular asset-based …
Seeing beyond the patch: Scale-adaptive semantic segmentation of high-resolution remote sensing imagery based on reinforcement learning
In remote sensing imagery analysis, patch-based methods have limitations in capturing
information beyond the sliding window. This shortcoming poses a significant challenge in …
information beyond the sliding window. This shortcoming poses a significant challenge in …
When urban region profiling meets large language models
Urban region profiling from web-sourced data is of utmost importance for urban planning
and sustainable development. We are witnessing a rising trend of LLMs for various fields …
and sustainable development. We are witnessing a rising trend of LLMs for various fields …
[HTML][HTML] China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with super …
Large-scale and multi-annual maps of building rooftop area (BRA) are crucial for addressing
policy decisions and sustainable development. In addition, as a fine-grained indicator of …
policy decisions and sustainable development. In addition, as a fine-grained indicator of …
[PDF][PDF] A machine learning framework for predicting and understanding the Canadian drought monitor
Drought is a costly natural disaster that impacts economies and ecosystems worldwide, so
monitoring drought and communicating its impacts to individuals, communities, industry, and …
monitoring drought and communicating its impacts to individuals, communities, industry, and …
Urbanclip: Learning text-enhanced urban region profiling with contrastive language-image pretraining from the web
Urban region profiling from web-sourced data is of utmost importance for urban computing.
We are witnessing a blossom of LLMs for various fields, especially in multi-modal data …
We are witnessing a blossom of LLMs for various fields, especially in multi-modal data …
Scale-aware deep reinforcement learning for high resolution remote sensing imagery classification
Abstract Land-use/land-cover (LULC) classification of high spatial resolution (HSR) remote
sensing imagery has been successfully improved using deep learning techniques. However …
sensing imagery has been successfully improved using deep learning techniques. However …
UrbanVLP: A Multi-Granularity Vision-Language Pre-Trained Foundation Model for Urban Indicator Prediction
Urban indicator prediction aims to infer socio-economic metrics in diverse urban landscapes
using data-driven methods. However, prevalent pre-trained models, particularly those reliant …
using data-driven methods. However, prevalent pre-trained models, particularly those reliant …
CBRA: The first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with Super-resolution Segmentation from …
Large-scale and multi-annual maps of building rooftop area (BRA) are crucial for addressing
policy decisions and sustainable development. In addition, as a fine-grained indicator of …
policy decisions and sustainable development. In addition, as a fine-grained indicator of …