Method for building segmentation and extraction from high-resolution remote sensing images based on improved YOLOv5ds. by Fangzhe Chang, Tianyue Ma, Dantong Wang, Shoujie Zhu, Dengping Li, Shuntian Feng, Xiaoyong Fan

“…To address challenges in remote sensing images, such as the abundance of buildings, difficulty in contour extraction, and slow update speeds, a high-resolution remote sensing image building segmentation and extraction method based on the YOLOv5ds network structure was proposed using Gaofen-2 images. …”
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Automatic Change Detection of High Resolution Remote Sensing Images Based on Level Set Evolution and Support Vector Machine Classification by YAN Ming, CAO Guo, XIA Meng

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Integrating unsupervised domain adaptation and SAM technologies for image semantic segmentation: a case study on building extraction from high-resolution remote sensing images by Mengyuan Yang, Rui Yang, Min Wang, Haiyan Xu, Gang Xu

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TPL-DA: A Novel Threshold-Free Pseudolabel Learning Framework for Domain Adaptive Semantic Segmentation of High-Resolution Remote Sensing Images by Yan Ren, Jie Long, Xiaowen Gao, Ming Zhang, Guoqing Liu, Nan Su

“…However, most methods highly depend on extensive annotated data, leading to performance deterioration in complex high-resolution remote sensing cross-domain scenes, where variations in image conditions and environments are prevalent. …”
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MSHRNet: a multi-scale high-resolution network for land cover classification from high spatial resolution remote sensing images by Fang Chen, Zhihui Ou, Congrong Li, Lei Wang, Bo Yu

“…To address these challenges, we propose the Multi-Scale High-Resolution Network (MSHRNet) for classifying ground objects from high-resolution remote sensing images. …”
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Dynamic atrous attention and dual branch context fusion for cross scale Building segmentation in high resolution remote sensing imagery by Yaohui Liu, Shuzhe Zhang, Xinkai Wang, Rui Zhai, Hu Jiang, Lingjia Kong

“…Abstract Building segmentation of high-resolution remote sensing images using deep learning effectively reduces labor costs, but still faces the key challenges of effectively modeling cross-scale contextual relationships and preserving fine spatial details. …”
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GreenNet: A dual-encoder network for urban green space classification using high-resolution remotely sensed images by Ke Chen, Yang Wang, Cunrui Huang, Jing Wang, Sabrina L. Li, Haiyan Guan, Lingfei Ma

“…Accurate classification of urban green spaces from high-resolution remotely sensed images is critical for ecological environment planning, construction, and utilization. …”
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RPFusionNet: An Efficient Semantic Segmentation Method for Large-Scale Remote Sensing Images via Parallel Region–Patch Fusion by Shiyan Pang, Weimin Zeng, Yepeng Shi, Zhiqi Zuo, Kejiang Xiao, Yujun Wu

“…Mainstream deep learning segmentation models are designed for small-sized images, and when applied to high-resolution remote sensing images, the limited information contained in small-sized images greatly restricts a model’s ability to capture complex contextual information at a global scale. …”
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Weighted Feature Fusion Network Based on Large Kernel Convolution and Transformer for Multi-Modal Remote Sensing Image Segmentation by Jianxia Wang, Shaozu Qiu, Jia Cai, Xiaoming Zhang

“…The heterogeneity and complexity of multi-modal data in high-resolution remote sensing images posed a severe challenge to existing cross-modal networks that aim to fuse complementary information of high-resolution optical and elevation data information (DSM) to achieve accurate semantic segmentation. …”
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Aquaculture Areas Extraction Model Using Semantic Segmentation from Remote Sensing Images at the Maowei Sea of Beibu Gulf by Weirong Qin, Mohd Hasmadi Ismail, Yangyang Luo, Yifeng Yuan, Junlin Deng, Mohammad Firuz Ramli, Ning Wu

“…The extraction of aquaculture areas from high-resolution remote sensing images is crucial for effective coastal management and resource preservation. …”
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Multiscale feature tuned trans-DeepLabV3+ based semantic segmentation of aerial images using improved red piranha optimization algorithm by P. Anilkumar, K. Lokesh, A. Naveen Kumar, D. John Pradeep, Y. V. Pavan Kumar, Rammohan Mallipeddi

Subjects: “…High-resolution aerial images…”
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ESSegNeXt: A Novel Building Extraction Framework Based on Generalized SamGeo and SegNeXt Models Using High-Resolution Remote Sensing Images by Qi Lu, Wensong Liu, Lianpeng Zhang, Erzhu Li, Junlong Qiu, Lei Wang

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Mapping the distribution of pine wilt disease based on selected machine learning algorithms and high-resolution Gaofen-2/7 remote sensing by Yifan Wang, Xiaocheng Zhou, Chongcheng Chen, Xiaoqin Wang, Hao Wu, Fanglin Tan, Ruijiao Wu

“…In key counties and cities, High-resolution satellite imagery (GF-2 and GF-7) was used to construct a bi-level scale-set model (BSM) for efficient image segmentation, followed by selection of the best classification algorithm for data extraction. …”
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A novel super-resolution urban green space segmentation network generating 0.2m resolution urban green space results using low-resolution imagery by Chunyang Chen, Guangbin Yang, Panfang Chen, Lei Zhan, Man Li, Juan Li

“…To address this, we propose SR-UGSnet, a super-resolution segmentation framework that reconstructs low-resolution remote sensing images using spatial redundancy from high-resolution data. …”
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Comparative Analysis of Multi-Resolution Remote Sensing Data for Accurate Road Segmentation in Urban Environments by M. R. Çevikalp, B. Mutlu, M. Yanalak, N. Musaoğlu

“…Besides, accurate mapping is essential for detecting road networks effectively, but traditional methods like manual digitization and field surveys often struggle in fast-changing urban environments. Remote sensing and deep learning techniques have emerged as effective alternatives, although initial road segmentation faced challenges such as limited image resolution. …”
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Urban Tree Canopy Mapping and Analysis Using Iterative Annotation Method and Deep Learning: A Case Study in Beijing by Yulong Ding, Ximin Cui, Zhengchao Chen, Zeqing Wang, Debao Yuan, Xiang Meng, Xuan Yang, Yue Xu, Xiangyu Tian

“…To comprehensively analyze the tree canopy characteristics of urban trees across large areas, this study selects Beijing as the research area, and employs high-resolution remote sensing imagery with deep learning techniques to construct the UTC map of Beijing. …”
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Extraction of Cropland Based on Multi-Source Remote Sensing and an Improved Version of the Deep Learning-Based Segment Anything Model (SAM) by Kunjian Tao, He Li, Chong Huang, Qingsheng Liu, Junyan Zhang, Ruoqi Du

“…Currently, semantic segmentation models based on high-resolution remote sensing imagery utilize limited spectral information and rely heavily on a large amount of fine data annotation, while pixel classification models based on medium-to-low-resolution multi-temporal remote sensing imagery are limited by the mixed pixel problem. …”
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Early detection of Citrus Huanglongbing by UAV remote sensing based on MGA-UNet by Naibo Ye, Wenyong Mai, Feng Qin, Sen Yuan, Bo Liu, Zaiyuan Li, Conghui Liu, Fanghao Wan, Wanqiang Qian, Zhongzhen Wu, Xi Qiao

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Swin-FSNet: A Frequency-Aware and Spatially Enhanced Network for Unpaved Road Extraction from UAV Remote Sensing Imagery by Jiwu Guan, Qingzhan Zhao, Wenzhong Tian, Xinxin Yao, Jingyang Li, Wei Li

“…The efficient recognition of unpaved roads from remote sensing (RS) images holds significant value for tasks such as emergency response and route planning in outdoor environments. …”
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TIMA-Net: A Lightweight Remote Sensing Image Change Detection Network Based on Temporal Interaction Enhancement and Multi-Scale Aggregation by Zhijun Zhou, Xuejie Zhang, Xiaoliang Luo, Lvchun Wang, Wei Yu, Shufang Xu, Longbao Wang

“…However, existing deep learning methods often face challenges of high computational complexity and insufficient detail capture, particularly demonstrating limited performance in detecting high-resolution images and complex change regions. To address these issues, this paper proposes a novel network architecture, TIMA-Net, which is designed for efficient remote sensing image change detection. …”
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