cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28777154.v1
title: An interpretable physics-informed deep learning model for estimating multiple air pollutants

description: Accurate and effective monitoring of atmospheric components is urgently needed due to the compound air pollution in China. Despite numerous efforts to estimate various air pollutant concentrations, the focus on individual pollutants has overlooked their interactions, thereby limiting estimation accuracy. In addition, the use of shared data and model structures in these studies has led to inefficiencies due to duplicated efforts. Moreover, widely applied machine learning models have demonstrated a lack of interpretability and a weak physical basis. To address these issues, this study developed an interpretable physics-informed deep neural network to estimate multiple air pollutants simultaneously (IPMDNN). The model inputs were initially categorized based on prior atmospheric knowledge. To account for the varying impact of each input on different pollutants, a tanh-based self-attention mechanism was followed to dynamically adjust their contributions. A deep interaction module was then introduced to capture the complex physicochemical interactions among pollutants. To ensure consistency with physical laws, a physics-constrained loss function was incorporated. Finally, layer-wise relevance propagation was employed to interpret the model results. Experiments conducted in China in 2019 and 2020 demonstrated that the proposed model effectively estimated ozone (O<sub>3</sub>) and particulate matter (PM<sub>2.5</sub> and PM<sub>10</sub>) concentrations simultaneously. Compared with separate estimation models, the proposed model achieved higher sample-based cross-validation R<sup>2</sup> values of 0.92, 0.90, and 0.87 for O<sub>3</sub>, PM<sub>2.5</sub>, and PM<sub>10</sub>, respectively, and demonstrated more than twice the efficiency. Furthermore, the model interpretation identified formaldehyde, carbon monoxide, hydroxyl radical, and temperature as key contributors in the joint estimation. Mapping results derived from the model highlighted severe O<sub>3</sub> and PM pollution in East, Central, and North China. In terms of seasonal trends, severe O<sub>3</sub> pollution was observed in the summer, whereas high levels of PM pollution occurred in the winter. These findings suggested that the proposed model, with its high accuracy and efficiency, could provide valuable insights for the coordinated control of air pollution in China.
DOI: 10.6084/m9.figshare.30885290
title: Performance and generalizability impacts of incorporating location encoders into deep learning for dynamic PM<sub>2.5</sub> estimation

description: Deep learning models have demonstrated success in geospatial applications, yet quantifying the role of geolocation information in enhancing model performance and geographic generalizability remains underexplored. A new generation of location encoders have emerged with the goal of capturing attributes present at any given location for downstream use in predictive modeling. Being a nascent area of research, their evaluation has remained largely limited to static tasks such as species distributions or average temperature mapping. In this paper, we discuss and quantify the impact of incorporating geolocation into deep learning for a real-world application domain that is characteristically dynamic (with fast temporal change) and spatially heterogeneous at high resolutions: estimating surface-level daily PM2.5 levels using remotely sensed and ground-level data. We build on a recently published deep learning-based PM2.5 estimation model that achieves state-of-the-art performance on data observed in the contiguous United States. We examine three approaches for incorporating geolocation: excluding geolocation as a baseline, using raw geographic coordinates, and leveraging pretrained location encoders. We evaluate each approach under within-region (WR) and out-of-region (OoR) evaluation scenarios. Aggregate performance metrics indicate that while naïve incorporation of raw geographic coordinates improves within-region performance by retaining the interpolative value of geographic location, it can hinder generalizability across regions. In contrast, pretrained location encoders like GeoCLIP enhance predictive performance and geographic generalizability for both WR and OoR scenarios. However, our qualitative analysis reveals artifact patterns caused by high-degree basis functions and sparse upstream samples in certain areas, and our ablation results indicate varying performance among location encoders such as SatCLIP vs. GeoCLIP. To the best of our knowledge, this is a first integration and systematic evaluation of location encoders in a complex, temporally dynamic estimation scenario. In addition to guiding better model development for air pollution estimation and location encoders, this study provides insights for effective incorporation of location into deep learning for geospatial predictive tasks.
DOI: 10.6084/m9.figshare.30885290.v1
title: Performance and generalizability impacts of incorporating location encoders into deep learning for dynamic PM<sub>2.5</sub> estimation

description: Deep learning models have demonstrated success in geospatial applications, yet quantifying the role of geolocation information in enhancing model performance and geographic generalizability remains underexplored. A new generation of location encoders have emerged with the goal of capturing attributes present at any given location for downstream use in predictive modeling. Being a nascent area of research, their evaluation has remained largely limited to static tasks such as species distributions or average temperature mapping. In this paper, we discuss and quantify the impact of incorporating geolocation into deep learning for a real-world application domain that is characteristically dynamic (with fast temporal change) and spatially heterogeneous at high resolutions: estimating surface-level daily PM2.5 levels using remotely sensed and ground-level data. We build on a recently published deep learning-based PM2.5 estimation model that achieves state-of-the-art performance on data observed in the contiguous United States. We examine three approaches for incorporating geolocation: excluding geolocation as a baseline, using raw geographic coordinates, and leveraging pretrained location encoders. We evaluate each approach under within-region (WR) and out-of-region (OoR) evaluation scenarios. Aggregate performance metrics indicate that while naïve incorporation of raw geographic coordinates improves within-region performance by retaining the interpolative value of geographic location, it can hinder generalizability across regions. In contrast, pretrained location encoders like GeoCLIP enhance predictive performance and geographic generalizability for both WR and OoR scenarios. However, our qualitative analysis reveals artifact patterns caused by high-degree basis functions and sparse upstream samples in certain areas, and our ablation results indicate varying performance among location encoders such as SatCLIP vs. GeoCLIP. To the best of our knowledge, this is a first integration and systematic evaluation of location encoders in a complex, temporally dynamic estimation scenario. In addition to guiding better model development for air pollution estimation and location encoders, this study provides insights for effective incorporation of location into deep learning for geospatial predictive tasks.
DOI: 10.6084/m9.figshare.30693805
title: LMSeg: an end-to-end geometric message-passing network on barycentric dualgraphs for large-scale landscape mesh segmentation

description: Semantic segmentation of large-scale 3D landscape meshes is critical for geospatial analysis in complex environments, yet existing approaches face persistent challenges of scalability, end-to-end trainability, and accurate segmentation of small and irregular objects. To address these issues, we introduce the BudjBim Wall (BBW) dataset, a large-scale annotated mesh dataset derived from high-resolution LiDAR scans of the UNESCO World Heritage-listed Budj Bim cultural landscape in Victoria, Australia. The BBW dataset captures historic dry-stone wall structures that are difficult to detect under vegetation occlusion, supporting research in underrepresented cultural heritage contexts. Building on this dataset, we propose LMSeg, a deep graph message-passing network for semantic segmentation of large-scale meshes. LMSeg employs a barycentric dual graph representation of mesh faces and introduces the Geometry Aggregation+ (GA+) module, a learnable softmax-based operator that adaptively combines neighborhood features and captures high-frequency geometric variations. A hierarchical-local dual pooling integrates hierarchical and local geometric aggregation to balance global context with fine-detail preservation. Experiments on three large-scale benchmarks (SUM, H3D, and BBW) show that LMSeg achieves 75.1% mIoU on SUM, 78.4% O.A. on H3D, and 62.4% mIoU on BBW, using only 2.4 M lightweight parameters and outperforming strong point- and graph-based baselines. In particular, LMSeg demonstrates accurate segmentation across both urban and natural scenes-capturing small-object classes such as vehicles and high vegetation in complex city environments, while also reliably detecting dry-stone walls in dense, occluded rural landscapes. Together, the BBW dataset and LMSeg provide a practical and extensible method for advancing 3D mesh segmentation in cultural heritage, environmental monitoring, and urban applications.
DOI: 10.6084/m9.figshare.30693805.v1
title: LMSeg: an end-to-end geometric message-passing network on barycentric dualgraphs for large-scale landscape mesh segmentation

description: Semantic segmentation of large-scale 3D landscape meshes is critical for geospatial analysis in complex environments, yet existing approaches face persistent challenges of scalability, end-to-end trainability, and accurate segmentation of small and irregular objects. To address these issues, we introduce the BudjBim Wall (BBW) dataset, a large-scale annotated mesh dataset derived from high-resolution LiDAR scans of the UNESCO World Heritage-listed Budj Bim cultural landscape in Victoria, Australia. The BBW dataset captures historic dry-stone wall structures that are difficult to detect under vegetation occlusion, supporting research in underrepresented cultural heritage contexts. Building on this dataset, we propose LMSeg, a deep graph message-passing network for semantic segmentation of large-scale meshes. LMSeg employs a barycentric dual graph representation of mesh faces and introduces the Geometry Aggregation+ (GA+) module, a learnable softmax-based operator that adaptively combines neighborhood features and captures high-frequency geometric variations. A hierarchical-local dual pooling integrates hierarchical and local geometric aggregation to balance global context with fine-detail preservation. Experiments on three large-scale benchmarks (SUM, H3D, and BBW) show that LMSeg achieves 75.1% mIoU on SUM, 78.4% O.A. on H3D, and 62.4% mIoU on BBW, using only 2.4 M lightweight parameters and outperforming strong point- and graph-based baselines. In particular, LMSeg demonstrates accurate segmentation across both urban and natural scenes-capturing small-object classes such as vehicles and high vegetation in complex city environments, while also reliably detecting dry-stone walls in dense, occluded rural landscapes. Together, the BBW dataset and LMSeg provide a practical and extensible method for advancing 3D mesh segmentation in cultural heritage, environmental monitoring, and urban applications.
DOI: 10.6084/m9.figshare.29205529
title: Assessing the impacts of urban expansion and climate change on net primary productivity over the past three decades in Beijing, China

description: Urban expansion and climate change have profoundly affected terrestrial net primary production (NPP), but their relative contributions to NPP changes across varied stages of urban development remain unclear. Furthermore, many studies were conducted at a low resolution (250–1000 m), which may increase the uncertainty of NPP estimates due to mixed pixels. This study used improved multisource data synergized quantitative (MuSyQ) NPP model to estimate NPP in Beijing during 1990–2020 at a 30 meters (m) resolution, and quantified the effects induced by urban expansion and climate change across different urban expansion stages and functional zones. The urban area in Beijing increased by 1823.91 km<sup>2</sup> over the study period, the fastest expansion rate occurred between 2000 and 2013, resulting in great NPP loss (18.21 GgCyear<sup>−1</sup>) (1 Gg = 10<sup>9</sup> g). The increasingly severe climate change has also led to a reduction in NPP, but this impact is much smaller than the direct loss of NPP caused by urban expansion. Especially between 2000 and 2013, the average annual NPP loss attributed to urban expansion is approximately 22.66 times greater than that caused by climate change. Our findings highlight the urgent need to develop regional strategies to address urban expansion, enhance natural carbon sinks, and achieve sustainable development.
DOI: 10.6084/m9.figshare.29205529.v1
title: Assessing the impacts of urban expansion and climate change on net primary productivity over the past three decades in Beijing, China

description: Urban expansion and climate change have profoundly affected terrestrial net primary production (NPP), but their relative contributions to NPP changes across varied stages of urban development remain unclear. Furthermore, many studies were conducted at a low resolution (250–1000 m), which may increase the uncertainty of NPP estimates due to mixed pixels. This study used improved multisource data synergized quantitative (MuSyQ) NPP model to estimate NPP in Beijing during 1990–2020 at a 30 meters (m) resolution, and quantified the effects induced by urban expansion and climate change across different urban expansion stages and functional zones. The urban area in Beijing increased by 1823.91 km<sup>2</sup> over the study period, the fastest expansion rate occurred between 2000 and 2013, resulting in great NPP loss (18.21 GgCyear<sup>−1</sup>) (1 Gg = 10<sup>9</sup> g). The increasingly severe climate change has also led to a reduction in NPP, but this impact is much smaller than the direct loss of NPP caused by urban expansion. Especially between 2000 and 2013, the average annual NPP loss attributed to urban expansion is approximately 22.66 times greater than that caused by climate change. Our findings highlight the urgent need to develop regional strategies to address urban expansion, enhance natural carbon sinks, and achieve sustainable development.
DOI: 10.6084/m9.figshare.29597768
title: SAR remote sensing for monitoring harmful algal blooms using deep learning models

description: Harmful Algal Blooms (HABs) threaten aquatic ecosystems, necessitating effective monitoring strategies in water resource management. Satellite-based remote sensing has emerged as a popular method to address the limitations of in-situ monitoring. However, cloud covers can obstruct optical imagery, causing data loss. Synthesis Aperture Radar (SAR) imagery, with its capabilities, can penetrate through any weather conditions. We applied SAR imagery with the Faster Regional Convolutional Neural Networks (Faster R-CNN) model to detect the algal bloom. The dataset of the Geum River Basin was obtained from 2020 to 2022. The sigma naught values (dB) were analyzed from the SAR imagery to clarify the reflectance properties of algae in VH and VV polarizations. The values ranged between −12 dB and −33 dB and −5 dB and −27 dB for VH and VV polarization, respectively. The model was developed with hyperparameter optimization to detect the algal bloom by splitting the training from 2020 to 2022, and the testing dataset 2022. Evaluation metrics including precision, recall, and F1 scores yielded values of 0.600, 0.692, and 0.643, respectively. The developed model was simulated to identify the seasonal outbreak. The result illustrated that the algal blooms were detected only in the summer of 2021 and 2022. Furthermore, the model was validated in supporting an existing algal alert report, demonstrating the potential for real-time monitoring. Finally, this study highlights the effectiveness of employing SAR imagery with the Faster R-CNN model to develop an algorithm for detecting algal blooms, offering advancements in water management practices.
DOI: 10.6084/m9.figshare.29597768.v1
title: SAR remote sensing for monitoring harmful algal blooms using deep learning models

description: Harmful Algal Blooms (HABs) threaten aquatic ecosystems, necessitating effective monitoring strategies in water resource management. Satellite-based remote sensing has emerged as a popular method to address the limitations of in-situ monitoring. However, cloud covers can obstruct optical imagery, causing data loss. Synthesis Aperture Radar (SAR) imagery, with its capabilities, can penetrate through any weather conditions. We applied SAR imagery with the Faster Regional Convolutional Neural Networks (Faster R-CNN) model to detect the algal bloom. The dataset of the Geum River Basin was obtained from 2020 to 2022. The sigma naught values (dB) were analyzed from the SAR imagery to clarify the reflectance properties of algae in VH and VV polarizations. The values ranged between −12 dB and −33 dB and −5 dB and −27 dB for VH and VV polarization, respectively. The model was developed with hyperparameter optimization to detect the algal bloom by splitting the training from 2020 to 2022, and the testing dataset 2022. Evaluation metrics including precision, recall, and F1 scores yielded values of 0.600, 0.692, and 0.643, respectively. The developed model was simulated to identify the seasonal outbreak. The result illustrated that the algal blooms were detected only in the summer of 2021 and 2022. Furthermore, the model was validated in supporting an existing algal alert report, demonstrating the potential for real-time monitoring. Finally, this study highlights the effectiveness of employing SAR imagery with the Faster R-CNN model to develop an algorithm for detecting algal blooms, offering advancements in water management practices.
DOI: 10.6084/m9.figshare.29663836
title: Can vegetation breakpoints in Eastern Mongolia rangeland be detected using Sentinel-1 coherence time series data?

description: Mongolian society and food production depend heavily on livestock farming, which is usually practiced through nomadic systems. Consequently, movement patterns of herders are crucial in respect of finding sufficient forage and sustainable use of pastures. Since vegetation presumably changes after livestock pasture use, this study hypothesizes that changes in Interferometric Synthetic Aperture Radar (InSAR) data over time are linked to herder and livestock mobility. In this study, a combination of InSAR, optical, and weather time series data has been explored as a tool for spatio-temporal grazing monitoring. To detect movement patterns, a new random forest-based method to detect breakpoints in vegetation condition has been developed and compared to the widely used <i>Breaks For Additive Season and Trend</i> (BFAST) algorithm. In contrast to BFAST, the new method accounts for vegetation changes caused by weather events such as snow and rainfall. The results have been validated using test sites spread across the entire eastern Mongolian steppe ecosystem, covering different rangeland use intensities. The results indicate that (1) random forest performed better than BFAST, indicating that random forest is able to separate vegetation changes caused by grazing from those caused by natural events. However, the detection was challenging especially for winter movements (for summer camps, random forest and BFAST detected 44% and 28% of movements, respectively). (2) Breakpoints in summer pastures mainly occurred from April to June, while on winter pastures, they emerged in October, November, and the following February and March. The breakpoints in October and November can be explained by increasing grazing pressure as the herders moved to the winter camps while those occurring in spring are associated with enhanced vegetation growth after herders left for summer camps. (3) From a spatial perspective, the random forest model predicts summer and winter pastures with homogeneous patterns. In areas with higher productivity and higher grazing pressure, the summer pastures are located along the rivers while the winter pastures are in the surrounding mountainous areas. This is in agreement with the general movement patterns. In drier and less intensively used areas, the predicted pattern agrees less with the known movements. Consequently, there is insufficient evidence to definitively attribute the occurrence of pasture breakpoints solely to herder movements especially in the eastern and southern parts of the eastern Mongolian steppe ecosystem.
DOI: 10.6084/m9.figshare.29663836.v1
title: Can vegetation breakpoints in Eastern Mongolia rangeland be detected using Sentinel-1 coherence time series data?

description: Mongolian society and food production depend heavily on livestock farming, which is usually practiced through nomadic systems. Consequently, movement patterns of herders are crucial in respect of finding sufficient forage and sustainable use of pastures. Since vegetation presumably changes after livestock pasture use, this study hypothesizes that changes in Interferometric Synthetic Aperture Radar (InSAR) data over time are linked to herder and livestock mobility. In this study, a combination of InSAR, optical, and weather time series data has been explored as a tool for spatio-temporal grazing monitoring. To detect movement patterns, a new random forest-based method to detect breakpoints in vegetation condition has been developed and compared to the widely used <i>Breaks For Additive Season and Trend</i> (BFAST) algorithm. In contrast to BFAST, the new method accounts for vegetation changes caused by weather events such as snow and rainfall. The results have been validated using test sites spread across the entire eastern Mongolian steppe ecosystem, covering different rangeland use intensities. The results indicate that (1) random forest performed better than BFAST, indicating that random forest is able to separate vegetation changes caused by grazing from those caused by natural events. However, the detection was challenging especially for winter movements (for summer camps, random forest and BFAST detected 44% and 28% of movements, respectively). (2) Breakpoints in summer pastures mainly occurred from April to June, while on winter pastures, they emerged in October, November, and the following February and March. The breakpoints in October and November can be explained by increasing grazing pressure as the herders moved to the winter camps while those occurring in spring are associated with enhanced vegetation growth after herders left for summer camps. (3) From a spatial perspective, the random forest model predicts summer and winter pastures with homogeneous patterns. In areas with higher productivity and higher grazing pressure, the summer pastures are located along the rivers while the winter pastures are in the surrounding mountainous areas. This is in agreement with the general movement patterns. In drier and less intensively used areas, the predicted pattern agrees less with the known movements. Consequently, there is insufficient evidence to definitively attribute the occurrence of pasture breakpoints solely to herder movements especially in the eastern and southern parts of the eastern Mongolian steppe ecosystem.