GRVIO: Semantic-Aware Visual-Inertial Odometry for Ground Robot Platforms by Sangbum Lee, Hanyeol Lee, Chan Gook Park

“…Simulation results demonstrate that each proposed measurement model enhances localization accuracy and show the robustness of the proposed method against semantic segmentation errors and extrinsic calibration uncertainties. …”
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PolSAR-SFCGN: An End-to-End PolSAR Superpixel Fully Convolutional Generation Network by Mengxuan Zhang, Jingyuan Shi, Long Liu, Wenbo Zhang, Jie Feng, Jin Zhu, Boce Chu

“…Addressing the above issues, this study proposes an end-to-end fully convolutional superpixel generation network for PolSAR images. …”
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Rice Leaf Nutrient Deficiency Classification System Using CAR-Capsule Network by M. Amudha, K. Brindha

“…Rice, a worldwide grown grain, frequently suffers production issues caused by nutrient imbalances, particularly potassium, nitrogen, and phosphorus. …”
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Federated LeViT-ResUNet for Scalable and Privacy-Preserving Agricultural Monitoring Using Drone and Internet of Things Data by Mohammad Aldossary, Jaber Almutairi, Ibrahim Alzamil

“…This study proposes a lightweight, hybrid deep learning architecture called federated LeViT-ResUNet that combines the spatial efficiency of LeViT transformers with ResUNet’s exact pixel-level segmentation to address these issues. The system uses multispectral drone footage and IoT sensor data to identify real-time insect hotspots, crop health, and yield prediction. …”
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STA-AgriNet: A Spatio-Temporal Attention Framework for Crop Type Mapping from Fused Multi-Sensor Multi-Temporal SITS by Jayakrishnan Anandakrishnan, Venkatesan Meenkaski Sundaram, Prabhavathy Paneer

“…Recent advancements in multisensor data fusion techniques have focused on integrating optical data with auxiliary synthetic aperture radar (SAR) data to mitigate misclassification issues. However, current optical-SAR fusion techniques have yet to effectively address the incorporation of spatial-spectral characteristics with long-term temporal dependencies of satellite image time series (SITS). …”
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Integrating Street View Images, Deep Learning, and sDNA for Evaluating University Campus Outdoor Public Spaces: A Focus on Restorative Benefits and Accessibility by Tingjin Wu, Deqing Lin, Yi Chen, Jinxiu Wu

“…This study applied the semantic segmentation technique of the deep learning model to extract the feature indicators of outdoor public space based on street view image (SVI) data. …”
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Assessing change point detection methods to enable robust detection of early stage Artisanal and Small-Scale mining (ASM) in the tropics using Sentinel-1 time series data by Mensah Isaac Obour, Barrett Brian, Cahalane Conor

“…Artisanal and Small-Scale mining (ASM) provides essential livelihoods for many in developing countries but often lacks regulation, leading to environmental issues such as water pollution and deforestation. …”
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TOPOLOGICAL IMAGE PROCESSING FOR COMPREHENSIVE DEFECT AND DEVIATION ANALYSIS USING ADAPTIVE BINARISATION by Igor Badanyuk, Igor Nevliudov, Dmytro Nikitin

“…This will allow solving the following issues: noise removal – removing noise from the image (noise can occur due to poor scan or photo quality, as well as due to the presence of small spots on the surface of the PCB); image segmentation – dividing the image into separate elements such as contours, zones and text (this process can be automated using image processing software); element detection – finding and separating individual elements such as resistors, capacitors and other components depicted on the topology. …”
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Stain Normalization of Histopathological Images Based on Deep Learning: A Review by Chuanyun Xu, Yisha Sun, Yang Zhang, Tianqi Liu, Xiao Wang, Die Hu, Shuaiye Huang, Junjie Li, Fanghong Zhang, Gang Li

“…However, color variations caused by differences in tissue preparation and scanning devices can lead to data distribution discrepancies, adversely affecting the performance of downstream algorithms in tasks like classification, segmentation, and detection. To address these issues, stain normalization methods have been developed to standardize color distributions across images from various sources. …”
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Road Pothole Recognition and Size Measurement Based on the Fusion of Camera and LiDAR by Yongxiang Cai, Mingxing Deng, Xin Xu, Wei Wang, Xiaowei Xu

“…Initially, we extract ground LiDAR point clouds from raw data through statistical filtering and ground segmentation. Subsequently, we generate a frustum as a two-dimensional (2D) region of interest to identify potential pothole areas within the ground LiDAR point clouds. …”
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Oriented Road Marking Detection in MLS Point Cloud Intensity Images Using Channel and Spatial Attention by Dehui Li, Tao Liu, Ping Du, Yi He, Shuangtong Liu

“…LSKNet extracts contextual information around the target by adaptively selecting receptive fields of various scales, while SEM, a lightweight semantic segmentation branch, further strengthens the feature representation ability of this information by incorporating a semantic supervision mechanism. …”
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Leaf‐wood classification of terrestrial laser scanning data with co‐registered near‐infrared photography by Luke A. Brown, Israa Kadhim, F. Mark Danson

“…A novel leaf‐wood classification approach is presented that avoids these issues by exploiting the spectral transmittance properties of leaves and wood, which, at near‐infrared wavelengths, demonstrate much larger differences than for reflectance. …”
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ES-Net Empowers Forest Disturbance Monitoring: Edge–Semantic Collaborative Network for Canopy Gap Mapping by Yutong Wang, Zhang Zhang, Jisheng Xia, Fei Zhao, Pinliang Dong

“…A cross-layer attention mechanism was embedded in the semantic segmentation module (SSM) to enhance the discriminative ability for heterogeneous features. …”
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MSFNet3D: Monocular 3D Object Detection via Dual-Branch Depth-Consistent Fusion and Semantic-Guided Point Cloud Refinement by Rong Yang, Zhijie You, Renhui Luo

“…However, existing pseudo-lidar methods encounter challenges such as coarse quality and insufficient semantic information when generating 3D point clouds from monocular images. To address these issues, this paper introduces MSFNet3D, which aims to overcome the quality limitations of pseudo-lidar point cloud. …”
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A Review of Open Remote Sensing Data with GIS, AI, and UAV Support for Shoreline Detection and Coastal Erosion Monitoring by Demetris Christofi, Christodoulos Mettas, Evagoras Evagorou, Neophytos Stylianou, Marinos Eliades, Christos Theocharidis, Antonis Chatzipavlis, Thomas Hasiotis, Diofantos Hadjimitsis

“…Despite the developments seen with these tools, issues relating to atmosphere such as cloud cover, data fusion, and model generalizability in different coastal environments continue to require resolutions to be addressed by future studies in terms of enhanced sensors and adaptive learning approaches with the rise of AI technology the recent years.…”
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Blockchain-Assisted Verifiable and Multi-User Fuzzy Search Encryption Scheme by Xixi Yan, Pengyu Cheng, Yongli Tang, Jing Zhang

“…In scenarios where multiple users can search for ciphertext, most fuzzy searchable encryption schemes ignore the security issues associated with malicious cloud services and are inflexible in multi-user scenarios. …”
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Research on Path Planning of Agricultural UAV Based on Improved Deep Reinforcement Learning by Haitao Fu, Zheng Li, Weijian Zhang, Yuxuan Feng, Li Zhu, Xu Fang, Jian Li

“…Traditional manual or semi-mechanized pesticide spraying methods often suffer from issues such as redundant coverage and cumbersome operational steps, which fail to meet current pest and disease control requirements. …”
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Loss Function Optimization Method and Unsupervised Extraction Approach D-DBSCAN for Improving the Moving Target Perception of 3D Imaging Sonar by Jingfeng Yu, Aigen Huang, Zhongju Sun, Rui Huang, Gao Huang, Qianchuan Zhao

“…This method automatically generates single-frame target detection boxes based on multi-frame joint segmentation, offering advantages in speed, cost, and accuracy. …”
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Retinopathy Disease Detection and Classification Using a Coordinate Attention Module-Based Convolutional Neural Network with Leaky Rectified Linear Unit by Pravin Balaso Chopade, Prabhakar N. Kota, Bhagvat D. Jadhav, Pravin Marotrao Ghate, Shriram Sadashiv Kulkarni

“…The existing techniques have limitations such as neuron death issues, vanishing gradient, and output offset. To overcome these issues, this paper proposes a Deep Learning (DL)-based technique for early and accurate DR detection. …”
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An investigation of Bayes algorithm and neural networks for identifying the breast cancer by E Udayakumar, S Santhi, P Vetrivelan

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