Jeng-Shyang Pan^1,2,3, Wenda Li², Shu-Chuan Chu^4,*, Zhi-Gang Du⁵, Hongmei Yang², Lingping Kong⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.084683 - 28 August 2026

Abstract Formulating efficient updating techniques is essential for the efficacy of metaheuristic algorithms. Traditional approaches, however, depend significantly on manually developed formulas and empirical intuition, which frequently constrain their adaptability and scalability across various optimization tasks. This research introduces a Genetic Programming-based Metaheuristic framework, referred to as GP-MAs, designed to autonomously develop and enhance symbolic update rules for metaheuristic algorithms. Within the suggested GP-MAs architecture, genetic programming (GP) is integrated into the learning phase of the Growth Optimizer (GO) to dynamically formulate symbolic update equations, hence enhancing the algorithm’s adaptability to diverse optimization landscapes. A hybrid… More >

Yufei Wang¹, Jiayi Shang¹, Fang Liu^1,*, Jun Liu²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085708 - 13 August 2026

Abstract Ship detection is an effective way of sea area supervision, which has important research value in both military and civil fields. For small ship targets in the sea scene, the deep feature map is difficult to effectively capture their subtle features, resulting in the decline of small target detection accuracy and the increase of the missing detection rate. To solve this problem, this paper proposes a detection algorithm called YOLO-MARALight, which adds a small target detection layer in the head network, uses a larger scale feature map to retain the details, and improves the discrimination… More > Graphic Abstract

Heng Wang¹, Shichao Li¹, Long Xu^2,*, Chuqiao Wang¹, Yanzhou Feng¹, Zou Zhou^1,3,4,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083432 - 13 August 2026

Abstract The inherent challenges of small objects in remote sensing imagery encompass the degradation of fine-grained spatial details throughout the downsampling stages, semantic inconsistency during multi-level feature fusion, along with unreliable localization caused by noisy samples. To address these issues, this paper proposes an efficient small-object detector termed EMW-YOLO. An Efficient Down-sampling (EDS) module is introduced to preserve fine-grained spatial information and enhance feature representation during feature extraction through spatial rearrangement and cross-dimensional attention. A Multi-Scale Fusion and Enhancement (MSFE) architecture is further developed to improve semantic consistency across feature levels by combining local enhancement with More >

Yusuf Ahmed Yusuf^1,2,3, Helmi Zulhaidi Mohd Shafri^1,3,*, Kamil Muhammad Kafi^4,5,*, Siti Nur Aliaa Roslan^1,3, Jibrin Gambo^3,6

Revue Internationale de Géomatique, Vol.35, pp. 491-508, 2026, DOI:10.32604/rig.2026.084692 - 07 August 2026

Abstract Urbanization alters land surface characteristics, intensifies urban heat, and degrades air quality, posing significant environmental and public health challenges. This study investigated the relationship between urban land surface temperature (LST) and air pollution in Kano Metropolis, Nigeria, using Earth observation data, geospatial techniques, and correlation analysis. Land use/land cover (LULC) analysis revealed that built-up areas account for 67.4% of the metropolitan area, contributing to elevated land surface temperatures, particularly within the densely urbanized local government areas of Kano Municipal, Gwale, Ungogo, Dala, and Fagge, as demonstrated by the LST and Urban Heat Island (UHI) analyses.… More >

Guangyu Xu^1,2, Yuxi Ban¹, Legend Zhang³, Junmin Lyu³, Feng Bao⁴, Wenfeng Zheng^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.085484 - 27 July 2026

Abstract High-resolution remote sensing semantic segmentation is a fundamental task in Geospatial Artificial Intelligence (GeoAI). Existing CNN-based methods are effective for local and multiscale feature extraction but often lack progressive cross-scale semantic propagation, while attention- and Transformer-based methods improve global spatial modeling but generally ignore frequency-domain regularities. To address these limitations, this study proposes a Multiscale Long-Distance Feature Aggregation Network (MLFANet), a unified spatial-frequency segmentation framework for high-resolution remote sensing imagery. MLFANet introduces three key components: a Multiscale Global Dependency Extraction module for cascaded cross-scale contextual refinement, an FFT-based frequency-domain branch with learnable global filtering for… More >

Khlieeq Ul Zaman^1,2,*, Ahmad Saeed³, Muhammad Awais Khan², Hafiz Abdul Basit⁴, Shaharyar², Maria Anum², Rani Ummay Farwa^2,*, Mahmood Iqbal¹, Ali Raza²

Revue Internationale de Géomatique, Vol.35, pp. 409-422, 2026, DOI:10.32604/rig.2026.083214 - 02 July 2026

Abstract Groundwater is the primary buffer against water scarcity in rapidly urbanizing regions, yet its sustainable management is constrained by limited hydrogeological data. This study presents an integrated Geographic Information System (GIS) remote sensing framework strengthened with Artificial Intelligence (AI) to delineate groundwater potential zones (GWPZs) in the Islamabad Capital Territory. Six thematic layers—slope, drainage density, lithology, rainfall, land use/land cover (LULC), and the Normalized Difference Vegetation Index (NDVI)—were derived from SRTM DEM, Sentinel-2 imagery, geological maps, and climate records. Each layer was standardized, reclassified, and weighted using the Analytical Hierarchy Process (AHP). A complete pairwise… More >

Mahmood Ashraf^1,2, Nuha Zamzami³, Shtwai Alsubai⁴, Raed Alharthi⁵, Muhammad Umer^6,*, Yunyoung Nam⁷, Yongwon Cho^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.078738 - 30 June 2026

Abstract Hyperspectral image (HSI) denoising is a crucial preprocessing step that significantly enhances the performance of downstream applications, such as object detection and classification. Whereas deep neural networks have achieved remarkable performance in HSI denoising, many existing models rely mostly on vanilla convolutions, which often fail to capture fine-grained noise patterns and structural details in real-time HSIs. To address these limitations, we propose a novel Center-Difference Convolutional Network (CDCN) designed to effectively suppress various noise types while preserving the inherent structure of HSIs. By leveraging center-difference convolution (CDC), our model captures both gradient and intensity information… More >

Shuaiyu Zhu¹, Sergey Ablameyko^1,2, Ji Li^3,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082486 - 15 June 2026

Abstract Satellite remote sensing images pose significant challenges for object detection due to their high resolution, complex scenes, and large variations in target scales. To address the insufficient detection accuracy of the YOLOv11n model in remote sensing imagery, this paper proposes two improvement strategies. Method 1: (a) a Large Separable Kernel Attention (LSKA) mechanism is introduced into the backbone network to enhance feature extraction for small objects; (b) a Gold-YOLO structure is incorporated into the neck network to achieve multi-scale feature fusion, thereby improving the detection performance of objects at different scales. Method 2: (a) the More >

Li Chen¹, Fan Zhang^2,*, Guangwei Xie³, Yanzhao Gao¹, Xiaofeng Qi¹, Mingqian Sun²

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.078314 - 15 June 2026

Abstract Remote sensing object detection aims to identify and localize specific targets in satellite or aerial imagery. Spiking Neural Networks (SNNs), benefiting from their implicit feedback-based and event-driven brain-inspired dynamics, offer a promising solution to alleviate the high energy consumption of conventional ANN-based detection models. However, existing SNN-based approaches for remote sensing object detection—particularly for small, arbitrarily rotated objects—are still in their infancy and suffer from a substantial performance gap compared with ANN counterparts. In this work, we draw inspiration from the hierarchical sparse perception mechanisms of biological vision and integrate dynamic receptive field modulation into… More >

Jesús Rodrigo-Comino^1,*, María Teresa González-Moreno¹, Lucía Moreno-Cuenca¹, Laura Cambronero-Ruiz¹, Clemente Irigaray², Francisco Serrano Bernardo³, Víctor Hugo Durán Zuazo⁴, Jesús Fernández-Gálvez⁵, Andrés Caballero-Calvo⁵, Víctor Rodríguez-Galiano⁶

Revue Internationale de Géomatique, Vol.35, pp. 333-349, 2026, DOI:10.32604/rig.2026.081503 - 11 June 2026

Abstract Soil degradation in Mediterranean agricultural systems is strongly conditioned by topography, water redistribution and solar exposure, factors that can be effectively studied using very high-resolution remote sensing. This study evaluates the potential of Unmanned Aerial Vehicle (UAV)-derived geomorphometry combined with machine learning techniques to analyse the spatial variability of the Normalized Difference Vegetation Index (NDVI) as a surface spectral response under post-harvest conditions in a Mediterranean cereal field affected by soil degradation and gully erosion, located near Casabermeja (Málaga, southern Spain). High-resolution RGB and multispectral UAV data were used to generate a Digital Terrain Model… More >