From Super-Resolution to Superior Land Cover Detection: Cross-Channel Attention Network for Aerial Image

Detection-Oriented Super-Resolution for Cost-Efficient Aerial Monitoring

Land Use and Land Cover Classification Using Transfer Learning and Temporal Convolutional Networks on Low-Resolution Remote Sensing Images

Recently, low-resolution remote sensing (RS) images have received significant attention because of their widespread spatial coverage, minimum acquisition cost, quick transmission ability, and large-scale earth observation suitability. However, land-use and land-cover (LULC) classification using low-resolution satellite imagery remains challenging due to restricted spatial information, spectral similarity amongst land-cover classes, noise differences, and complex scene heterogeneity. Though recent deep learning-based models have exhibited effective outcomes, they still suffer from insufficient feature representation, inadequate contextual dependency learning, and minimal classification accuracy when processing low-resolution RS images. To resolve these issues, this study develops a lightweight feature extraction model with Temporal Convolutional Networks for low-resolution remote sensing image classification. The proposed model initially preprocesses the images to improve feature consistency and quality. The feature extraction phase then employs MobileNet-V2 to identify and represent relevant spatial patterns in RS images, followed by a temporal convolutional network for RSI classification, enabling effective modeling of sequential and contextual dependencies in spatial features. Furthermore, adaptive fine-tuning of model parameters is performed using an artificial rabbit optimization algorithm to enhance classification accuracy and convergence behavior. Extensive experimental evaluation of the LFEARO-LULCRSI model on the benchmark EuroSat Dataset from Sentinel-2 imagery demonstrates improved performance over existing methods, achieving an accuracy of 98.57%. An ablation study is also performed to examine the contribution of individual model components. The proposed model thus proves useful for effective geospatial analysis in agriculture, urban planning, disaster assessment, and sustainable environmental management, enhancing feature discrimination and contextual dependency learning in low-resolution satellite imagery.

SOD-MSC: Multi-Scale Context-Focused Detection for Small Traffic Objects in Aerial Images

The detection of small objects in aerial traffic scenes is vital for advancing intelligent transportation systems (ITS), yet traditional methods often suffer from scale variation, background complexity, and insufficient context utilization. To address these challenges, we propose SOD-MSC, a YOLOv8-based small object detector designed for remote sensing images. SOD-MSC integrates a global multi-scale (GMS) module to enhance global perception and multi-scale fusion, a lightweight fusion feature pyramid network (LFFPN) to strengthen spatial correlations and suppress background noise, and an adaptive RT-DETR-based detection head with Inner_SIoU loss to improve localization accuracy and convergence. Experiments on VisDrone2019-DET and HazyDet demonstrate that SOD-MSC achieves 39.9% and 49% on mAP@0.5, with only 5.22 M parameters and 11.2 GFLOPs, surpassing several baselines and state-of-the-art models in balancing accuracy and efficiency. These results highlight the effectiveness of SOD-MSC for dense small object detection in complex traffic environments, with promising applications in congestion monitoring, vehicle counting, and related ITS tasks.

FlowT-SR: A Novel Remote Sensing Image Super-Resolution Framework with Cloud Haze and Noise Suppression

A novel SR framework based on the flow matching paradigm and a diffusion transformer, named FlowT-SR, which achieves superior and reliable reconstruction quality by jointly mitigating sensor noise and thin cloud interference, achieving superior reconstruction performance compared with current state-of-the-art methods in terms of both PSNR and SSIM.

Forest Road Extraction from High-Resolution Remote Sensing Imagery Based on an Improved U-Net Model

To address the challenges of vegetation interference, background confusion, and road fragmentation caused by the narrow and elongated structures of forest roads in complex remote sensing imagery, this study proposed an improved U-Net-based model, namely HAA-UNet, for automatic forest road extraction. The proposed model integrates a VGG16 encoder, an Atrous Spatial Pyramid Pooling (ASPP) module, and a Hybrid Dilated Convolution (HDCC) module to enhance feature representation and spatial detail reconstruction of road targets under complex forest environments. Experimental results demonstrated that HAA-UNet achieved superior segmentation performance on the forest road dataset of Xichang City, Sichuan Province, with Precision, Recall, F1-score, and mIoU values of 87.24%, 87.83%, 87.53%, and 79.85%, respectively, outperforming all comparison models. These results indicate that the proposed method effectively improves road continuity and boundary delineation in complex forest scenes. Furthermore, the extracted road information was integrated into the Forest Fire Risk Index (FFRI) assessment framework, demonstrating that accurate road data can improve the spatial characterization of fire risk and provide reliable data support for forest fire risk assessment and forest resource management.

A Multi-Stage Deep Learning Framework for Shadow Detection in Aerial Orthophotos

Abstract. Shadow correction is an important preprocessing step not only for visual enhancement but also for improving object recognition performance in remote sensing imagery. Although many datasets and deep learning models have been proposed for shadow detection and removal, most of them focus on natural images. In contrast, high-resolution aerial orthophotos contain large continuous shadows caused by tall buildings, especially in urban areas, and existing models often fail to handle such large-scale structures effectively.In this study, we construct a new shadow annotation dataset specifically designed for aerial orthophotos with spatial resolutions of 20 cm/pixel and 5 cm/pixel. Furthermore, we propose a three-stage multi-resolution segmentation framework that progressively refines shadow predictions from low to high resolution. Predictions from lower-resolution stages are used as auxiliary information to guide higher-resolution prediction. Experimental results demonstrate that the proposed approach improves fuzzy Intersection over Union (IoU) by approximately 0.05 compared with a previously published shadow detection model, and also outperforms a single-stage baseline, particularly for large continuous shadow regions. The framework is also applicable to other large-scale segmentation tasks requiring extensive receptive fields.