[HTML][HTML] Deep learning for urban land use category classification: A review and experimental assessment
Mapping the distribution, pattern, and composition of urban land use categories plays a
valuable role in understanding urban environmental dynamics and facilitating sustainable …
valuable role in understanding urban environmental dynamics and facilitating sustainable …
From single-to multi-modal remote sensing imagery interpretation: A survey and taxonomy
Modality is a source or form of information. Through various modal information, humans can
perceive the world from multiple perspectives. Simultaneously, the observation of remote …
perceive the world from multiple perspectives. Simultaneously, the observation of remote …
Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks
Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-
modality-dominated remote sensing (RS) applications, especially with an emphasis on …
modality-dominated remote sensing (RS) applications, especially with an emphasis on …
Hyperspectral and SAR image classification via multiscale interactive fusion network
Due to the limitations of single-source data, joint classification using multisource remote
sensing data has received increasing attention. However, existing methods still have certain …
sensing data has received increasing attention. However, existing methods still have certain …
Domain adaptation in remote sensing image classification: A survey
Traditional remote sensing (RS) image classification methods heavily rely on labeled
samples for model training. When labeled samples are unavailable or labeled samples have …
samples for model training. When labeled samples are unavailable or labeled samples have …
[HTML][HTML] Enabling country-scale land cover mapping with meter-resolution satellite imagery
High-resolution satellite images can provide abundant, detailed spatial information for land
cover classification, which is particularly important for studying the complicated built …
cover classification, which is particularly important for studying the complicated built …
A review of landcover classification with very-high resolution remotely sensed optical images—Analysis unit, model scalability and transferability
As an important application in remote sensing, landcover classification remains one of the
most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly …
most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly …
[HTML][HTML] Cross-spatiotemporal land-cover classification from VHR remote sensing images with deep learning based domain adaptation
M Luo, S Ji - ISPRS Journal of Photogrammetry and Remote …, 2022 - Elsevier
Automatic land use/land cover (LULC) classification from very high resolution (VHR) remote
sensing images can provide us with rapid, large-scale, and fine-grained understanding of …
sensing images can provide us with rapid, large-scale, and fine-grained understanding of …
[HTML][HTML] A review and meta-analysis of generative adversarial networks and their applications in remote sensing
Abstract Generative Adversarial Networks (GANs) are one of the most creative advances in
Deep Learning (DL) in recent years. The Remote Sensing (RS) community has adopted …
Deep Learning (DL) in recent years. The Remote Sensing (RS) community has adopted …
Artificial intelligence to advance Earth observation: a perspective
Earth observation (EO) is a prime instrument for monitoring land and ocean processes,
studying the dynamics at work, and taking the pulse of our planet. This article gives a bird's …
studying the dynamics at work, and taking the pulse of our planet. This article gives a bird's …