Geospatial dataset on deforestation and urban sprawl in Dhaka, Bangladesh: A resource for environmental analysisMendeley Data by Md. Fahad Khan, Md. Rakibul Islam, Shanto Kumar Basak, Ahmed Imtiaz, Abhijit Bhowmik, Dip Nandi, Mashiour Rahman, Debajyoti Karmaker

“…It provides critical insights into the application of machine learning and image processing in addressing real-world environmental issues.…”
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NMD-FusionNet: a multimodal fusion-based medical imaging-assisted diagnostic model for liver cancer by Qing Ye, Minghao Luo, Jing Zhou, Chunlei Cheng, Lin Peng, Jia Wu

“…While deep learning models like ResUNet have improved diagnostic performance, issues such as information loss and inaccurate tumor boundary segmentation persist. …”
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Historical habitat mapping from black-and-white aerial photography: A proof of concept for post World War II Switzerland by Nica Huber, Matthias Bürgi, Christian Ginzler, Birgit Eben, Andri Baltensweiler, Bronwyn Price

“…A random forest model was trained to classify the segments using variables describing spectral information, image texture, segment shape, topography, climate, and anthropogenic influence. …”
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Semi-MeshSeg: A semi-supervised semantic segmentation network for large-scale urban textured meshes using all pseudo-labels by Wenjie Zi, Jun Li, Hao Chen, Qingren Jia

“…Urban mesh data comprises large-scale textured meshes representing outdoor urban environments. Semantic segmentation of urban meshes is becoming increasingly important in urban analysis. …”
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A fast and accurate segmentation model of lychee tree canopy from UAV remote sensing image based on big data and deep learning by Jianhua Wang, Hongyi Xiong

“…To address this issue, this paper proposes a fast and accurate segmentation model for lychee tree canopies from UAV remote sensing images based on big data and deep learning technologies. …”
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Posterior Percutaneous Endoscopic Cervical Discectomy for Single-Segment Cervical Spondylotic Radiculopathy: A Retrospective Study with Minimum 3-Year Follow-Up by Wang X, Li T, Li Y, Long Y

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Automated Audit and Self-Correction Algorithm for Seg-Hallucination Using MeshCNN-Based On-Demand Generative AI by Sihwan Kim, Changmin Park, Gwanghyeon Jeon, Seohee Kim, Jong Hyo Kim

“…Recent advancements in deep learning have significantly improved medical image segmentation. However, the generalization performance and potential risks of data-driven models remain insufficiently validated. …”
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Multiclass Crop Interpretation via a Lightweight Attentive Feature Fusion Network Using Vehicle-View Images by Wenyue Li, Bingfang Wu, Runyu Fan, Fuyou Tian, Miao Zhang, Zhaoying Zhou, Jun Hu, Ruyi Feng, Fangming Wu

“…To address the abovementioned issues, this article proposes a novel lightweight neural network, namely the CropNet, for crop interpretation. …”
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Las reducciones toledanas a pueblos de indios: aproximación a un conflicto by Carolina Jurado

“…The quest of Toledan re-grouping into new towns («pueblos de reducción») becomes a main issue to understand the organization of ethnic groups and the construction of a new colonial andean territoriality. …”
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AP-PointRend: An Improved Network for Building Extraction via High-Resolution Remote Sensing Images by Bowen Zhu, Ding Yu, Xiongwu Xiao, Jian Shen, Zhigao Cui, Yanzhao Su, Aihua Li, Deren Li

“…However, due to the complex and diverse structures of buildings and their interconnections, the accuracy of extracted buildings remains insufficient for high-precision applications such as maps and navigation. To address the issue of enhancing building boundary extraction, we propose a modified instance segmentation model, AP-PointRend (Adaptive Parameter-PointRend), to improve the performance of building instance extraction. …”
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A Multi-Task Learning Framework with Enhanced Cross-Level Semantic Consistency for Multi-Level Land Cover Classification by Shilin Tao, Haoyu Fu, Ruiqi Yang, Leiguang Wang

“…The results indicate that MTL-SCH improves classification accuracy in coarse- and fine-level segmentation tasks, significantly enhancing semantic consistency across levels and outperforming traditional flat segmentation methods.…”
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Application of quantitative histomorphometric features in computational pathology by Yujie Shi, Bo Hu, Mingyan Xu, Yunhan Yao, Shuaiqiang Gao, Xiang Xia, Xikai Deng, Jianfeng Liu, Jia Gu, Shifu Chen

“…However, these methods are often criticized for the “black box” issue, a significant limitation of DL. In contrast, traditional feature engineering‐based methods involve well‐defined quantitative input features. …”
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Serum markers of cardiac fibrosis suffering from heart failure with preserved left ventricular ejection fraction upon ST-segment elevation myocardial infarction by A. V. Оsokina, V. N. Karetnikova, O. M. Polikutina, O. V. Gruzdeva, T. P. Solodilova, S. N. Kosareva, A. V. Motova, T. B. Pecherina, O. L. Barbarash

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Remaining Useful Life Interval Prediction for Lithium-Ion Batteries via Periodic Time Series and Trend Filtering Segmentation-Based Fuzzy Information Granulation by Chunsheng Cui, Guangshu Xia, Chenyu Jia, Jie Wen

“…In this paper, a construction method of periodic time series is applied to the degradation data of LiBs to address the issues of insufficient training data and smooth degradation in the RUL interval prediction method based on trend filtering segmentation and fuzzy information granulation. …”
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Multi-Scale Convolutional Attention and Structural Re-Parameterized Residual-Based 3D U-Net for Liver and Liver Tumor Segmentation from CT by Ziwei Song, Weiwei Wu, Shuicai Wu

“…Accurate segmentation of the liver and liver tumors is crucial for clinical diagnosis and treatment. …”
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SWRD–YOLO: A Lightweight Instance Segmentation Model for Estimating Rice Lodging Degree in UAV Remote Sensing Images with Real-Time Edge Deployment by Chunyou Guo, Feng Tan

“…However, Unmanned Aerial Vehicle (UAV)-based lodging detection faces challenges such as complex backgrounds, variable lighting, and irregular lodging patterns. To address these issues, this study proposes SWRD–YOLO, a lightweight instance segmentation model that enhances feature extraction and fusion using advanced convolution and attention mechanisms. …”
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AASNet: A Novel Image Instance Segmentation Framework for Fine-Grained Fish Recognition via Linear Correlation Attention and Dynamic Adaptive Focal Loss by Jianlei Kong, Shunong Tang, Jiameng Feng, Lipo Mo, Xuebo Jin

“…However, underwater fish recognition faces challenges in complex aquatic environments, which hinder accurate detection and behavioral analysis. To address these issues, we propose a novel image instance segmentation framework based on a deep learning neural network, defined as the AASNet (Agricultural Aqua Segmentation Network). …”
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AER U-Net: attention-enhanced multi-scale residual U-Net structure for water body segmentation using Sentinel-2 satellite images by Naga Surekha Jonnala, Shaik Siraaj, Y. Prastuti, P. Chinnababu, B. Praveen babu, Shonak Bansal, Prashant Upadhyaya, Krishna Prakash, Mohammad Rashed Iqbal Faruque, K. S. Al-mugren

“…These difficulties have a significant impact on waterbody segmentation, particularly in precisely maintaining high-quality segmented images and determining the boundaries of waterbodies. …”
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NIR-RGB-M²Net: A Fusion Model for Precise Agricultural Field Segmentation Using Multisource Remote Sensing Data by Zhankui Tang, Xin Pan, Xiangfei She, Jian Zhao

“…Multisource remote sensing combines near-infrared (NIR) and visible light (RGB) data to leverage complementary features, but fusing these modalities often requires complex networks that risk losing vegetation signals and boundary details. To address this issue, this article proposes NIR-RGB-M²Net, a novel fusion model for precise agricultural field segmentation using multisource remote sensing data. …”
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Adaptive multilevel attention deeplabv3+ with heuristic based frame work for semantic segmentation of aerial images using improved golden jackal optimization algorithm by Anilkumar P, Venugopal P, Satheesh Kumar S, Jagannadha Naidu K

“…Semantic segmentation of small-scale objects in aerial images is challenging due to low-level characteristics in neural networks and varying information in feature maps. …”
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