Advances in Sensor Data Fusion and AI for Environmental Monitoring

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Data data	2.4	5.4	2016	19.2 Days	CHF 1600	Submit
Geosciences geosciences	2.3	4.4	2011	22.7 Days	CHF 1800	Submit
ISPRS International Journal of Geo-Information ijgi	3.2	6.7	2012	34.9 Days	CHF 1900	Submit
Remote Sensing remotesensing	4.3	9.4	2009	22 Days	CHF 2700	Submit
Sensors sensors	4.0	9.4	2001	17.8 Days	CHF 2600	Submit

21 pages, 14585 KB

Open AccessArticle

Fault Diagnosis of Motor Bearing Transmission System Based on Acoustic Feature Fusion

by Long Ma, Yan Zhang, Zhongqiu Wang and Bohao Niu

Sensors 2026, 26(17), 5671; https://doi.org/10.3390/s26175671 - 7 Sep 2026

Viewed by 366

Bearings are crucial components in motor bearing transmission systems because they reduce friction and support loads. Therefore, bearing fault diagnosis is particularly important. This paper proposes a fault diagnosis method for motor bearing transmission systems based on acoustic signals and acoustic feature fusion. [...] Read more.

Bearings are crucial components in motor bearing transmission systems because they reduce friction and support loads. Therefore, bearing fault diagnosis is particularly important. This paper proposes a fault diagnosis method for motor bearing transmission systems based on acoustic signals and acoustic feature fusion. The complete acoustic signal is segmented, and seven time-series imaging methods, including Gramian Angular Difference Field (GADF) and Gramian Angular Summation Field (GASF), are used to convert one-dimensional signals into two-dimensional feature maps. The generated images are then input into a RegNet-based transfer learning network. According to the single-feature training results, the feature map datasets ranking in the top two, three, and four are selected for feature fusion to construct new datasets. The results obtained under the present experimental setup indicate that acoustic feature fusion can improve the diagnostic performance compared with using a single feature map dataset. After comprehensive comparison, the dataset generated by summing two feature maps, namely STFT and Mel spectrogram, is selected as the final input dataset in this study. The current work focuses on a fixed operating condition, and further validation under different speeds, loads, sensor positions, background noise levels, bearing models, and defect severities will be conducted in future work. Full article

(This article belongs to the Topic Advances in Sensor Data Fusion and AI for Environmental Monitoring)

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29 pages, 3701 KB

Open AccessArticle

Geometry Guided Adaptive Ray-Surface Intersection for LiDAR Camera Fusion Spatial Localization

by Zishuo Lian, Jun Wu and Honglin Chen

ISPRS Int. J. Geo-Inf. 2026, 15(9), 385; https://doi.org/10.3390/ijgi15090385 - 26 Aug 2026

Viewed by 250

Abstract

In LiDAR-camera fusion measurement, conventional nearest-neighbor back-projection methods are limited by discrete point-cloud sampling and struggle to accurately recover the spatial position corresponding to image measurement points under sparse point-cloud conditions. Moreover, targets in complex industrial environments often exhibit diverse local geometric structures, [...] Read more.

In LiDAR-camera fusion measurement, conventional nearest-neighbor back-projection methods are limited by discrete point-cloud sampling and struggle to accurately recover the spatial position corresponding to image measurement points under sparse point-cloud conditions. Moreover, targets in complex industrial environments often exhibit diverse local geometric structures, making it difficult for a single surface model to effectively describe different surface characteristics. This paper proposes a Geometry-Guided Adaptive Ray-Surface Intersection measurement method, which replaces the traditional image-to-discrete-point mapping with ray-to-continuous-surface intersection. A spatial viewing ray is constructed from the image measurement point and camera optical center, and local neighborhood points are extracted through nearest projected-point back-projection. Based on curvature and normal-vector statistical features, local surfaces are classified into four geometric categories, and corresponding surface models are adaptively constructed using plane fitting, curved surface fitting, voxel-assisted plane searching, or bilinear patch fitting. The three-dimensional coordinates are then obtained through ray-model intersection. Experiments were conducted on multiple target types in an industrial equipment warehouse over a range of 5–20 m. The results show that the proposed method reduces the average measurement error from 5.63 cm of the nearest-neighbor method to 1.81 cm. When the point-cloud density decreases to 25%, the average absolute error increases by only 26.5%, compared with 92.6% and 58.7% for the nearest-neighbor and interpolation methods, respectively. These results demonstrate the effectiveness and robustness of the proposed method for LiDAR-camera fusion measurement under sparse point-cloud conditions. Full article

(This article belongs to the Topic Advances in Sensor Data Fusion and AI for Environmental Monitoring)

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19 pages, 2870 KB

Open AccessArticle

A Hybrid ARIMA-CNN-LSTM Framework Based on Serial Decomposition for Non-Stationary Water Level Forecasting in Qinghai Lake

by Pengfei Hou, Jingxu Wang, Shike Qiu, Shuangquan Li, Xiang Jia, Yangguang Li, Danni He, Yufeng Ma, Di Zhang and Jun Du

ISPRS Int. J. Geo-Inf. 2026, 15(6), 263; https://doi.org/10.3390/ijgi15060263 - 12 Jun 2026

Viewed by 525

Abstract

Qinghai Lake, the largest endorheic saline lake in China, has undergone a pronounced hydrological regime shift from a multi-decadal decline to a rapid post-2004 recovery, reflecting strong hydroclimatic non-stationarity in the northeastern Tibetan Plateau (TP). This paper supplements the current water level and [...] Read more.

Qinghai Lake, the largest endorheic saline lake in China, has undergone a pronounced hydrological regime shift from a multi-decadal decline to a rapid post-2004 recovery, reflecting strong hydroclimatic non-stationarity in the northeastern Tibetan Plateau (TP). This paper supplements the current water level and lake area status of Qinghai Lake to provide basic background for future prediction. Reliable forecasting of such climate sensitive lake systems remains difficult because conventional statistical models often fail to capture non-linear fluctuations, whereas standalone deep learning models may overlook long-term deterministic evolution. To address this challenge, we developed a serial decomposition GeoAI framework that integrates autoregressive integrated moving average (ARIMA), one-dimensional convolutional neural networks (1D-CNNs), and long short-term memory (LSTM) networks for non-stationary water level forecasting. Using annual water level observations from 1960 to 2025, the ARIMA component was first used to extract the low-frequency deterministic trend, after which the CNN-LSTM module reconstructed the nonlinear residual variability. The model was trained on the 1960–2012 period and validated over 2013–2025, which represents the most dynamic expansion stage of Qinghai Lake. The hybrid framework outperformed the benchmark models, achieving a Root Mean Square Error (RMSE) of 0.2033 m, Mean Absolute Error (MAE) of 0.1727 m, and Mean Squared Error (MSE) of 0.0413 m² during validation. The decomposition strategy effectively reduced phase lag and amplitude attenuation, improving both predictive accuracy and process interpretability. Multi-step forecasting for 2026–2056 suggests that Qinghai Lake will continue to rise, reaching approximately 3204.08 m by 2056, although the growth rate is projected to slow as negative hydrological feedback strengthen. By explicitly separating deterministic climate scale signals from nonlinear short-term variability, the proposed framework provides a robust and transferable geoinformation based tool for forecasting water level dynamics and supporting adaptive management in climate sensitive, data scarce lake basins. Full article

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21 pages, 11108 KB

Open AccessArticle

Noise-Aware Diffusion for City-Scale Air-Quality Reconstruction from Sparse Monitoring Stations

by Guanglei Zheng, Yuchai Wan, Xun Zhang and Xiansheng Liu

ISPRS Int. J. Geo-Inf. 2026, 15(4), 171; https://doi.org/10.3390/ijgi15040171 - 14 Apr 2026

Cited by 1 | Viewed by 1267

Abstract

Reliable air-quality monitoring is essential for urban exposure assessment and environmental policy, yet many downstream applications are hindered by sparse regulatory stations and noisy real-world measurements. While diffusion models have shown promise for probabilistic spatiotemporal imputation, common conditioning strategies can be brittle: purely [...] Read more.

Reliable air-quality monitoring is essential for urban exposure assessment and environmental policy, yet many downstream applications are hindered by sparse regulatory stations and noisy real-world measurements. While diffusion models have shown promise for probabilistic spatiotemporal imputation, common conditioning strategies can be brittle: purely input-based conditioning may drift from sparse constraints, whereas hard clamping can introduce a clean–noisy mismatch and propagate corrupted readings during reverse sampling. In this work, we propose STGPD (SpatioTemporal Graph Posterior Diffusion), a probabilistic framework that formulates city-scale pollutant reconstruction as posterior sampling on a graph-structured spatiotemporal field. STGPD enforces noise-aware soft consistency by re-noising visible observations to the current diffusion level and fusing a noise-matched measurement term with the model prior via variance-weighted fusion under an explicit observation-noise model. To improve spatial extrapolation in heterogeneous urban environments, we further construct a dual-view graph that combines geographic proximity with functional similarity derived from static descriptors. Experiments on real-world monitoring data in Augsburg, Germany, for PM₁₀ and NO₂ show that STGPD provides a robust probabilistic reconstruction framework under extreme sparsity, station outages, and synthetic sensor-noise injection in this sparse-monitoring case study. Compared with strong deterministic and diffusion-based baselines, STGPD achieves improved reconstruction accuracy (MAE/RMSE) and better-calibrated uncertainty estimates (CRPS) under the current evaluation protocols. Full article

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35 pages, 19390 KB

Open AccessArticle

Dense Local Azimuth–Elevation Map for the Integration of GIS Data and Camera Images

by Gilbert Maître

ISPRS Int. J. Geo-Inf. 2026, 15(3), 131; https://doi.org/10.3390/ijgi15030131 - 16 Mar 2026

Viewed by 1033

Abstract

The integration of outdoor camera images with three-dimensional (3D) geographic information on the observed scene is of interest for many video acquisition applications. To solve this data fusion problem, camera images have to be matched with the 3D geometry provided by a geographic [...] Read more.

The integration of outdoor camera images with three-dimensional (3D) geographic information on the observed scene is of interest for many video acquisition applications. To solve this data fusion problem, camera images have to be matched with the 3D geometry provided by a geographic information system (GIS). Considering a camera with a known geographical position, this paper proposes the use of a dense local azimuth–elevation map (LAEM) derived from a gridded digital elevation model (DEM) to represent the data and thus facilitate the matching of GIS and image data. To each regularly sampled azimuth and elevation angle pair, this map assigns the geographic point derived from the DEM viewed in this direction. The problem of computing the LAEM from the DEM is closely related to that of surface rendering, for which solutions exist in computer graphics. However, rendering software cannot be used directly in this case, since their view directions are constrained by the pinhole camera model and the apparent colour, rather than the position of the viewed point, is assigned to the viewing direction. Therefore, this paper also proposes a specific algorithm for the computation of the LAEM from the DEM. A MATLAB^® implementation of the algorithm is also provided, which is tailored to process the DEM dataset swissALTI3D from the Swiss Federal Office of Topography swisstopo. Full article

(This article belongs to the Topic Advances in Sensor Data Fusion and AI for Environmental Monitoring)

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25 pages, 15438 KB

Open AccessArticle

Day–Night All-Sky Scene Classification with an Attention-Enhanced EfficientNet

by Wuttichai Boonpook, Peerapong Torteeka, Kritanai Torsri, Daroonwan Kamthonkiat, Yumin Tan, Asamaporn Sitthi, Patcharin Kamsing, Chomchanok Arunplod, Utane Sawangwit, Thanachot Ngamcharoensuktavorn and Kijnaphat Suksod

ISPRS Int. J. Geo-Inf. 2026, 15(2), 66; https://doi.org/10.3390/ijgi15020066 - 3 Feb 2026

Viewed by 2346

Abstract

All-sky cameras provide continuous hemispherical observations essential for atmospheric monitoring and observatory operations; however, automated classification of sky conditions in tropical environments remains challenging due to strong illumination variability, atmospheric scattering, and overlapping thin-cloud structures. This study proposes EfficientNet-Attention-SPP Multi-scale Network (EASMNet), a [...] Read more.

All-sky cameras provide continuous hemispherical observations essential for atmospheric monitoring and observatory operations; however, automated classification of sky conditions in tropical environments remains challenging due to strong illumination variability, atmospheric scattering, and overlapping thin-cloud structures. This study proposes EfficientNet-Attention-SPP Multi-scale Network (EASMNet), a physics-aware deep learning framework for robust all-sky scene classification using hemispherical imagery acquired at the Thai National Observatory. The proposed architecture integrates Squeeze-and-Excitation (SE) blocks for radiometric channel stabilization, the Convolutional Block Attention Module (CBAM) for spatial–semantic refinement, and Spatial Pyramid Pooling (SPP) for hemispherical multi-scale context aggregation within a fully fine-tuned EfficientNetB7 backbone, forming a domain-aware atmospheric representation framework. A large-scale dataset comprising 122,660 RGB images across 13 day–night sky-scene categories was curated, capturing diverse tropical atmospheric conditions including humidity, haze, illumination transitions, and sensor noise. Extensive experimental evaluations demonstrate that the EASMNet achieves 93% overall accuracy, outperforming representative convolutional (VGG16, ResNet50, DenseNet121) and transformer-based architectures (Swin Transformer, Vision Transformer). Ablation analyses confirm the complementary contributions of hierarchical attention and multi-scale aggregation, while class-wise evaluation yields F1-scores exceeding 0.95 for visually distinctive categories such as Day Humid, Night Clear Sky, and Night Noise. Residual errors are primarily confined to physically transitional and low-contrast atmospheric regimes. These results validate the EASMNet as a reliable, interpretable, and computationally feasible framework for real-time observatory dome automation, astronomical scheduling, and continuous atmospheric monitoring, and provide a scalable foundation for autonomous sky-observation systems deployable across diverse climatic regions. Full article

(This article belongs to the Topic Advances in Sensor Data Fusion and AI for Environmental Monitoring)

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21 pages, 12301 KB

Open AccessArticle

Visual Localization Algorithm with Dynamic Point Removal Based on Multi-Modal Information Association

by Jing Ni, Boyang Gao, Hongyuan Zhu, Minkun Zhao and Xiaoxiong Liu

ISPRS Int. J. Geo-Inf. 2026, 15(2), 60; https://doi.org/10.3390/ijgi15020060 - 30 Jan 2026

Cited by 1 | Viewed by 1176

Abstract

To enhance the autonomous navigation capability of intelligent agents in complex environments, this paper presents a visual localization algorithm for dynamic scenes that leverages multi-source information fusion. The proposed approach is built upon an odometry framework integrating LiDAR, camera, and IMU data, and [...] Read more.

To enhance the autonomous navigation capability of intelligent agents in complex environments, this paper presents a visual localization algorithm for dynamic scenes that leverages multi-source information fusion. The proposed approach is built upon an odometry framework integrating LiDAR, camera, and IMU data, and incorporates the YOLOv8 model to extract semantic information from images, which is then fused with laser point cloud data. We design a dynamic point removal method based on multi-modal association, which links 2D image masks to 3D point cloud regions, applies Euclidean clustering to differentiate static and dynamic points, and subsequently employs PnP-RANSAC to eliminate any remaining undetected dynamic points. This process yields a robust localization algorithm for dynamic environments. Experimental results on datasets featuring dynamic objects and a custom-built hardware platform demonstrate that the proposed dynamic point removal method significantly improves both the robustness and accuracy of the visual localization system. These findings confirm the feasibility and effectiveness of our system, showcasing its capabilities in precise positioning and autonomous navigation in complex environments. Full article

(This article belongs to the Topic Advances in Sensor Data Fusion and AI for Environmental Monitoring)

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17 pages, 4983 KB

Open AccessArticle

TAGNet: A Tidal Flat-Attentive Graph Network Designed for Airborne Bathymetric LiDAR Point Cloud Classification

by Ahram Song

ISPRS Int. J. Geo-Inf. 2025, 14(12), 466; https://doi.org/10.3390/ijgi14120466 - 28 Nov 2025

Cited by 1 | Viewed by 947

Abstract

Airborne LiDAR bathymetry (ALB) provides dense three-dimensional point clouds that enable the detailed mapping of tidal flat environments. However, surface classification using these point clouds remains challenging due to residual noise, water surface reflectivity, and subtle class boundaries that persist even after standard [...] Read more.

Airborne LiDAR bathymetry (ALB) provides dense three-dimensional point clouds that enable the detailed mapping of tidal flat environments. However, surface classification using these point clouds remains challenging due to residual noise, water surface reflectivity, and subtle class boundaries that persist even after standard preprocessing. To address these challenges, this study introduces Tidal flat-Attentive Graph Network (TAGNet), a graph-based deep learning framework designed to leverage both local geometric relationships and global contextual cues for the point-wise classification of tidal flat surface classes. The model incorporates multi-scale EdgeConv layers for capturing fine-grained neighborhood structures and employs squeeze-and-excitation channel attention to enhance global feature representation. To validate TAGNet’s effectiveness, classification was conducted on ALB point clouds collected from adjacent tidal flat regions, focusing on four major surface classes: exposed flat, sea surface, sea floor, and vegetation. In benchmarking tests against baseline models, including Dynamic Graph Convolutional Neural Network, PointNeXt with Single-Scale Grouping, and PointNet Transformer, TAGNet consistently achieved higher macro F1-scores. Moreover, ablation studies isolating positional encoding, attention mechanisms, and detrended Z-features confirmed their complementary contributions to TAGNet’s performance. Notably, the full TAGNet outperformed all baselines by a substantial margin, particularly when distinguishing closely related classes, such as sea floor and exposed flat. These findings highlight the potential of graph-based architectures specifically designed for ALB data in enhancing the precision of coastal monitoring and habitat mapping. Full article

(This article belongs to the Topic Advances in Sensor Data Fusion and AI for Environmental Monitoring)

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24 pages, 10966 KB

Open AccessArticle

UAV-Based Wellsite Reclamation Monitoring Using Transformer-Based Deep Learning on Multi-Seasonal LiDAR and Multispectral Data

by Dmytro Movchan, Zhouxin Xi, Angeline Van Dongen, Charumitha Selvaraj and Dani Degenhardt

Remote Sens. 2025, 17(20), 3440; https://doi.org/10.3390/rs17203440 - 15 Oct 2025

Cited by 1 | Viewed by 1751

Abstract

Monitoring reclaimed wellsites in boreal forest environments requires accurate, scalable, and repeatable methods for assessing vegetation recovery. This study evaluates the use of uncrewed aerial vehicle (UAV)-based light detection and ranging (LiDAR) and multispectral (MS) imagery for individual tree detection, crown delineation, and [...] Read more.

Monitoring reclaimed wellsites in boreal forest environments requires accurate, scalable, and repeatable methods for assessing vegetation recovery. This study evaluates the use of uncrewed aerial vehicle (UAV)-based light detection and ranging (LiDAR) and multispectral (MS) imagery for individual tree detection, crown delineation, and classification across five reclaimed wellsites in Alberta, Canada. A deep learning workflow using 3D convolutional neural networks was applied to LiDAR and MS data collected in spring, summer, and autumn. Results show that LiDAR alone provided high accuracy for tree segmentation and height estimation, with a mean intersection over union (mIoU) of 0.94 for vegetation filtering and an F1-score of 0.82 for treetop detection. Incorporating MS data improved deciduous/coniferous classification, with the highest accuracy (mIoU = 0.88) achieved using all five spectral bands. Coniferous species were classified more accurately than deciduous species, and classification performance declined for trees shorter than 2 m. Spring conditions yielded the highest classification accuracy (mIoU = 0.93). Comparisons with ground measurements confirmed a strong correlation for tree height estimation (R² = 0.95; root mean square error = 0.40 m). Limitations of this technique included lower performance for short, multi-stemmed trees and deciduous species, particularly willow. This study demonstrates the value of integrating 3D structural and spectral data for monitoring forest recovery and supports the use of UAV remote sensing for scalable post-disturbance vegetation assessment. The trained models used in this study are publicly available through the TreeAIBox plugin to support further research and operational applications. Full article

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27 pages, 16753 KB

Open AccessArticle

A 1°-Resolution Global Ionospheric TEC Modeling Method Based on a Dual-Branch Input Convolutional Neural Network

by Nian Liu, Yibin Yao and Liang Zhang

Remote Sens. 2025, 17(17), 3095; https://doi.org/10.3390/rs17173095 - 5 Sep 2025

Cited by 2 | Viewed by 2199

Abstract

Total Electron Content (TEC) is a fundamental parameter characterizing the electron density distribution in the ionosphere. Traditional global TEC modeling approaches predominantly rely on mathematical methods (such as spherical harmonic function fitting), often resulting in models suffering from excessive smoothing and low accuracy. [...] Read more.

Total Electron Content (TEC) is a fundamental parameter characterizing the electron density distribution in the ionosphere. Traditional global TEC modeling approaches predominantly rely on mathematical methods (such as spherical harmonic function fitting), often resulting in models suffering from excessive smoothing and low accuracy. While the 1° high-resolution global TEC model released by MIT offers improved temporal-spatial resolution, it exhibits regions of data gaps. Existing ionospheric image completion methods frequently employ Generative Adversarial Networks (GANs), which suffer from drawbacks such as complex model structures and lengthy training times. We propose a novel high-resolution global ionospheric TEC modeling method based on a Dual-Branch Convolutional Neural Network (DB-CNN) designed for the completion and restoration of incomplete 1°-resolution ionospheric TEC images. The novel model utilizes a dual-branch input structure: the background field, generated using the International Reference Ionosphere (IRI) model TEC maps, and the observation field, consisting of global incomplete TEC maps coupled with their corresponding mask maps. An asymmetric dual-branch parallel encoder, feature fusion, and residual decoder framework enables precise reconstruction of missing regions, ultimately generating a complete global ionospheric TEC map. Experimental results demonstrate that the model achieves Root Mean Square Errors (RMSE) of 0.30 TECU and 1.65 TECU in the observed and unobserved regions, respectively, in simulated data experiments. For measured experiments, the RMSE values are 1.39 TECU and 1.93 TECU in the observed and unobserved regions. Validation results utilizing Jason-3 altimeter-measured VTEC demonstrate that the model achieves stable reconstruction performance across all four seasons and various time periods. In key-day comparisons, its STD and RMSE consistently outperform those of the CODE global ionospheric model (GIM). Furthermore, a long-term evaluation from 2021 to 2024 reveals that, compared to the CODE model, the DB-CNN achieves average reductions of 38.2% in STD and 23.5% in RMSE. This study provides a novel dual-branch input convolutional neural network-based method for constructing 1°-resolution global ionospheric products, offering significant application value for enhancing GNSS positioning accuracy and space weather monitoring capabilities. Full article

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