bluuebunny/crossref_metadata_2025 huggingface dataset:
MATCHING PERSISTENT SCATTERER CLUSTERS TO BUILDING ELEMENTS IN MESH REPRESENTATION
Authors: Schneider P. J. , Soergel U.
Published: May 2022

Abstract. The deformation time series of Persistent Scatterer points show a correlated behavior if they lay on the same rigid structure and therefore undergo the same deformation process. By clustering, such groups of Persistent Scatterer (PS) points can be identified. We use segmented mesh representations of single buildings, to find the optimal assignment of such clusters to parts of the structure. By applying a quality metric, the assignment is judged quantitatively. The proposed method is useful for the integration of PSInSAR into building information modeling (BIM) and aims at a cost-effective city-wide per-building monitoring.
HYPERSPECTRAL IMAGE CLASSIFICATION WITH LOCALIZED SPECTRAL FILTERING-BASED GRAPH ATTENTION NETWORK
Authors: Pu S. , Song Y. , Chen Y. , Li Y. , Zhang J. , Lin Q. , Zhu X. , Chen Y. , Zeng H. , Liao K. , Yu H. , Yuan J. , Yu S. , Zuo W.
Published: May 2022

Abstract. Graph-based deep learning has been proved a promising approach that has an apparent superiority for learning graph data and modeling spatial topological relations between features. In particular, graph attention networks (GATs) are good at efficiently processing the graph-structured hyperspectral data by leveraging masked self-attention layers to address the known shortcomings of previous frameworks based on graph convolutions or their approximations. In this study, we proposed a novel approach that combines localized spectral filtering and GAT for the hyperspectral image classification task. First, we conducted unsupervised t-SNE (t-distributed stochastic neighbor embedding) manifold learning-based feature dimensionality reduction to create localized hyperspectral data cubes. Then, these feature cubes combined with localized adjacent matrices were fed into a shallow graph attention network in a supervised learning manner. Finally, we obtained credible classification results and promising classification performance in distinguishing diversified land covers through reducing the possible redundancy of spectral information and enhancing the expression of local spatial-spectral information. Experiments on two real hyperspectral data sets (that is, Indian Pines-A (IA) and Huanghekou (HH) data sets) demonstrated that the presented approach offers promising classification performance, that is, the GAT using t-SNE acquires superior performance than that of using PCA (principal component analysis), and also proves the great importance of combining spatial- and spectral information for hyperspectral image classification.
DMPCONV: DECOUPLING MULTI-BRANCH POINTWISE CONVOLUTIONS FOR LIGHT-WEIGHT REMOTE SENSING SCENE CLASSIFICATION
Authors: Hou J. , Guo Z. , Wu Y. , Diao W. , Feng Y.
Published: May 2022

Abstract. The use of multi-branch architectures in off-the-shelf light-weight residual series neural networks can significantly improve their performance in remote sensing scene classification tasks. However, such architectures come at the expense of an increased number of parameters and calculations. In this paper, we propose the Decoupling Multi-branch Pointwise Convolutions (DMPConv), which works without a corresponding increase in parameters and calculations during inferencing, and at the same time, can maintain the same performance improvement ability as the multi-branch architectures. DMPConv can be decoupled into two states, the training-time DMPConv and the inferencing-time DMPConv. The training-time DMPConv enhances the expressivity of the network by using weighted multi-branch 1×1 convolutions. After training, we use structural reconstruction to convert the training-time DMPConv to the inferencing-time DMPConv, which has the same form as vanilla 1×1 convolution, so as to realize the inferencing-free. Extensive experiments were conducted on multiple remote sensing scene classification benchmarks, including Aerial Image data set and NWPU-RESISC45 data set to demonstrate the superiority of DMPConv.
INVESTIGATING 2D AND 3D CONVOLUTIONS FOR MULTITEMPORAL LAND COVER CLASSIFICATION USING REMOTE SENSING IMAGES
Authors: Voelsen M. , Teimouri M. , Rottensteiner F. , Heipke C.
Published: May 2022

Abstract. With the availability of large amounts of satellite image time series (SITS), the identification of different materials of the Earth’s surface is possible with a high temporal resolution. One of the basic tasks is the pixel-wise classification of land cover, i.e. the task of identifying the physical material of the Earth’s surface in an image. Fully convolutional neural networks (FCN) are successfully used for this task. In this paper, we investigate different FCN variants, using different methods for the computation of spatial, spectral, and temporal features. We investigate the impact of 3D convolutions in the spatial-temporal as well as in the spatial-spectral dimensions in comparison to 2D convolutions in the spatial dimensions only. Additionally, we introduce a new method to generate multitemporal input patches by using time intervals instead of fixed acquisition dates. We then choose the image that is closest in time to the middle of the corresponding time interval, which makes our approach more flexible with respect to the requirements for the acquisition of new data. Using these multi-temporal input patches, generated from Sentinel-2 images, we improve the classification of land cover by 4% in the mean F1-score and 1.3% in the overall accuracy compared to a classification using mono-temporal input patches. Furthermore, the usage of 3D convolutions instead of 2D convolutions improves the classification performance by a small amount of 0.4% in the mean F1-score and 1.2% in the overall accuracy.
POSITION-SENSITIVE ATTENTION BASED ON FULLY CONVOLUTIONAL NEURAL NETWORKS FOR LAND COVER CLASSIFICATION
Authors: Xiong Z. , Zhan Z. , Wang X.
Published: May 2022

Abstract. Pixel-wise land cover classification is a fundamental task in remote sensing image interpretation, aiming to identify planimetric features (e.g., trees, waters, buildings etc.) from earth's surface. Recently, deep learning methods based on fully convolutional neural networks (FCN) become the mainstream approach for land cover classification, thanks to their superior performance in the image context perception and features learning. However, for high-resolution remote sensing images with huge quantity of object details, some deep learning based methods often ignore many important details by nature, specially, in the procedure of pooling operation and stacking convolutions in conventional FCN, it can leads to ambiguous classification of adjacent objects. To refine lost details caused by the stacking convolutions, we propose a position-sensitive attention (PSA) based on skip connections for land cover classification with high-resolution remote sensing images, which designs to deliver a weight that is sensitive to the spatial details in remote sensing images, the PSA module is able to improve pixel-level details scattered across spatial positions. Experimental results demonstrate that our method can be feasible to existing FCN-based models, 1% improvement in F1-score is obtained on 2021 "Shengteng Cup" competition dataset after using PSA, when comparing to several state-of-the-art methods, similar or even better performance is achieved on the ISPRS Vaihingen 2D dataset, but with less parameters.
CHANGES OF SPATIAL DISPARITIES IN SATELLITE-DERIVED PM2.5 EXPOSURES OVER 2010–2019 IN CHINA
Authors: Liu M. , Zhou G. , Liu Z. , Han L. , Li J.
Published: May 2022

Abstract. Air pollution, especially fine particulate matter (PM2.5), has attracted extensive attention due to its adverse impacts on public health. Although PM2.5 pollution was significantly reduced in China over time, while little is known how the spatial disparity of PM2.5 exposure has evolved, especially from both absolute and relative perspectives. Here, we estimate the long-term PM2.5 exposures in China based on satellite observations and convolutional neural network, and characterize the spatial disparity of PM2.5 exposure using Theil index and rank-rank relationship. The result shows that both PM2.5 exposure and absolute spatial disparity were substantially reduced between 2010 and 2019. The nation-wide concentrations (Theil index) declined from 48.0µg/m3 (0.13) to 35.5µg/m3 (0.054). The inter-provincial disparities dominate the overall disparity in 2010, while the intra-provincial disparity contributed the most in 2019. However, while absolute disparities have diminished, relative disparities persist. PM2.5 exposures in the least 20th percentile polluted cities have increased over time, while exposures in other regions declined. On average, the more (less) polluted cities in 2010 were still the more (less) polluted cities in 2019 (except for the very most 2 percentile polluted cities), indicating that the population in more polluted cities still experiences more air pollution than others. Spatial pattern of relative disparity changes was also observed. Overall, understanding not only absolute spatial disparity but also relative disparity is required to help formulate targeted policies for an equitable environment, leaving nobody behind.
ONE DAY AHEAD PREDICTION OF PM2.5 SPATIAL DISTRIBUTION USING MODIS 3 KM AOD AND SPATIOTEMPORAL MODEL OVER BEIJING-TIANJIN-HEBEI, CHINA
Authors: Wang X. , Liu W. , Sun W. , Peng Y. , Zhang Y. , Zhai X. , Zhao T. , Li R.
Published: May 2022

Abstract. Accurate prediction of PM2.5 concentration is the premise and guarantees to effectively control PM2.5 concentration and avoid the adverse effects of high PM2.5 concentration on human health. However, given the existing statistical models can only predict the pollutant concentration at the monitoring sites, the spatial distribution of PM2.5 concentration cannot be predicted, which greatly limits the application of the model in PM2.5 concentration prediction. This study combined the PM2.5 spatial distribution data predicted using the Moderate Resolution Imaging Spectroradiometer 3 km aerosol optical depth (AOD) and meteorological factors into a spatiotemporal autoregressive (STAR) model to predict the regional PM2.5 concentration and quantify the short-term spatial distribution change of PM2.5 in Beijing–Tianjin–Hebei region (JingJinJi) one day in advance. Five simulation functions were used to simulate the STAR model, and the 2014 data of JingJinJi were used to verify its accuracy. Results showed that the STAR model had the best prediction performance when gradient boosting decision tree was used as the simulation function compared with other simulation functions. The coefficient of determination (R2), root mean square prediction error (RMSE), index of agreement (IA), and mean absolute error (MAE) of the STAR model were 0.85, 27.08 µg/m3, 0.96, and 20 µg/m3, respectively. The spatial distribution prediction results of PM2.5 showed that the +1-day PM2.5 spatial distribution prediction results were in good agreement with the PM2.5 spatial distribution results predicted by AOD to provide accurate spatiotemporal distribution data for reducing air pollution and air pollution early warning.
FUSION OF MULTI-TEMPORAL AND MULTI-SENSOR ICE VELOCITY OBSERVATIONS
Authors: Charrier L. , Yan Y. , Colin Koeniguer E. , Mouginot J. , Millan R. , Trouvé E.
Published: May 2022

Abstract. Ice velocity observations available on-line or on-demand at intra-annual resolution still contain gaps, noise, and artifacts, especially in mountain areas. There is a need to fuse the available multi-temporal and multi-sensor velocity observations to be able to study intra-annual glacier dynamics. The proposed approach includes an inversion based on the temporal closure of displacement observation networks and a temporal interpolation. It reconstructs velocity time series between consecutive dates at a regular temporal sampling (called Regular Leap Frog (RLF) time series) inferred from all the velocity observations without a prori knowledge on the displacement behavior. The RLF time series can be reconstructed for different temporal sampling. Root Mean Square Error (RMSE) over stable areas and Velocity Vector Coherence (VVC) over fast moving areas are proposed to select a temporal sampling allowing a compromise between uncertainty and temporal resolution. This study focuses on the Fox glacier, in the Southern Alps of New Zealand. It shows that RMSE over stable areas is decreased from 78% for a temporal sampling of 5 days to 40% for a temporal sampling of 60 days. Thus, using this approach, we obtain a velocity time series with a complete temporal coverage and reduced uncertainty for a regular and optimal temporal sampling. The results highlight the large seasonal variability of the flow of Fox Glacier that fluctuates by more than 30% between spring and autumn.
STUDY ON SNOW COVER CHANGE BASED ON THE FUSION OF SENTINEL-2 AND MODIS IMAGES
Authors: Liu Q. , Zhang Y.
Published: May 2022

Abstract. Snow cover is one of the features that change rapidly on the surface, and remote sensing data with high spatial and temporal resolution is an important means to monitor the dynamic changes of snow. However, due to the limitation of satellite conditions, it is difficult to acquire remote sensing images with both high temporal and spatial resolution at the same time. The classical Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) breaks through the limitations of a single sensor and effectively brings into play the complementary advantages of different observation platforms. In this paper, we take the Babao River Basin within the Qinghai-Tibet Plateau as the study area to investigate the effect of fusion of Sentinel-2 and MODIS (Moderate-resolution Imaging Spectroradiometer) based on the ESTARFM and analyze the fusion-supplemented snow accumulation period data. The results show that the fusion effect of Sentinel-2 and MODIS is good in terms of visual and multiple quantitative indicators. The snow accumulation area of the fused image is also close to the real image. By fusing to supplement the missing Sentinel-2 data, a variation map of snow NDSI (Normalized Difference Snow Index) in the study area was obtained. It was found that the snowfall process in the watershed appeared first in the surrounding uplands and then in the flat areas, and the maximum rate of snow cover area change was 116.17 km2. Therefore, the spatiotemporal data fusion of Sentinel-2 and MODIS based on ESTARFM algorithm can provide reliable satellite remote sensing data with a higher spatiotemporal resolution for snow accumulation change monitoring.
SATELLITE OBSERVATIONS REVEAL MORPHOLOGICAL CHANGES IN ASIAN DELTAS FROM 1986 TO 2018
Authors: Chen C. , Zhang C. , Tian B. , Zhou Y.
Published: May 2022

Abstract. Asian deltas are densely populated and richly bio-diversified regions with significant social, economic, ecological and environmental importance across the globe. These low-lying and fertile floodplains support substantial food production. Because of sediment supply reductions, sea-level rising, subsidence, and frequent storm surges, many deltas are rapidly sinking and shrinking, which will threaten survival of millions of populations. It’s of great significance to monitor the delta morphological evolution and understand the relationship between their shoreline migrations and sediment supplies. However, quantifying the delta morphological changes is challenging due to its nature of high dynamics. In this study, we developed a methodology integrated by a time-series algorithm of water frequency and a spatial analysis to track the morphological changes of 11 major Asian deltas from 1986 to 2018.The results show that the deltas of Yellow, Yangtze, Pearl, Red, Mekong, Ganges-Brahmaputra, and Chao Phraya have experienced a net land gain at the total of 1829.3 km2, with China’s three major deltas accounting for 73 percent of all. The deltas of Mahanadi, Irrawaddy, Indus, and Godavari-Krishna have undergone a net land loss at the total of 295.1 km2, of which the Indus River Delta accounted for 65 percent. Most of the shorelines in the Yangtze River Delta were constantly advancing seaward at a rate of over 30 m/year, whereas, many ones in the rest of Asian deltas were significantly being eroded at a rate of over 20 m/year. These changes in delta morphology are closely related to sediment supplies and management policies. These results also suggest that rational wetland reclamation with auxiliary projects of ecological restoration can enhance the deltaic ability of sustainable development.
SOIL MOISTURE PREDICTION WITH MULTISPECTRAL VISIBLE AND NIR REMOTE SENSING
Authors: McGuirk S. L. , Cairns I. H.
Published: May 2022

Abstract. Water is a valuable resource and an understanding of soil moisture dynamics is critical in many land management, agricultural and engineering applications. Satellite and UAV remote sensing platforms present an opportunity for rapid, cost-efficient data collection; however, soil moisture remote sensing presents unique challenges. Specifically, spectral bands near 1400nm and 1900nm associated with water are typically avoided in remote sensing data products due to strong interference by atmospheric moisture. Using soil reflectance data collected in the lab, this paper presents a number of linear equations which maybe be applied to predict soil moisture content from Landsat 5 MSS, 7 TM and 9 data, as well as other NIR sensors collecting data at 1720, 1782, 2140 and 2240nm.
DETECTION OF DEGRADED ACACIA TREE SPECIES USING DEEP NEURAL NETWORKS ON UAV DRONE IMAGERY
Authors: Osio A. A. , Lê H.-Â. , Ayugi S. , Onyango F. , Odwe P. , Lefèvre S.
Published: May 2022

Abstract. Deep-learning-based image classification and object detection has been applied successfully to tree monitoring. However, studies of tree crowns and fallen trees, especially on flood inundated areas, remain largely unexplored. Detection of degraded tree trunks on natural environments such as water, mudflats, and natural vegetated areas is challenging due to the mixed colour image backgrounds. In this paper, Unmanned Aerial Vehicles (UAVs), or drones, with embedded RGB cameras were used to capture the fallen Acacia Xanthophloea trees from six designated plots around Lake Nakuru, Kenya. Motivated by the need to detect fallen trees around the lake, two well-established deep neural networks, i.e. Faster Region-based Convolution Neural Network (Faster R-CNN) and Retina-Net were used for fallen tree detection. A total of 7,590 annotations of three classes on 256×256 image patches were used for this study. Experimental results show the relevance of deep learning in this context, with Retina-Net model achieving 38.9% precision and 57.9% recall.
CHARACTERIZING THE DROUGHT DEVELOPMENT IN THE PHILIPPINES USING MULTIPLE DROUGHT INDICES DURING THE 2019 EL NIÑO
Authors: Perez G. J. , Enricuso O. , Manauis K. , Valete M. A.
Published: May 2022

Abstract. Drought events in the Philippines have resulted in a significant loss in crop production. This study investigated the drought development during the 2019 weak El Niño using different drought indices at a country and local scale. Satellite data from MODIS were used to derive Normalized Difference Vegetation Index (NDVI) anomaly, Vegetation Health Index (VHI), and Standardized Vegetation and Temperature Ratio (SVTR), while TRMM was used for the Standardized Precipitation Index (SPI). These indices were compared with crop production data at national scale and crop damage reports at local scale. The results showed consistency in the spatiotemporal variation of drought events, where drought peak occurred during March to April in most indices and indicators. Based on the values of SPI, the areas with rainfall deficit increased from January to April (25% to almost 100%), but looking at vegetation stress, around 50% was affected, as seen by SVTR and VHI. Yield loss during the first and second quarter after the El Niño peak increased up to 20%, especially in the western region in the Philippines. Overall findings demonstrate the relevance of utilizing multiple drought indices and indicators that characterize drought evolution, from drought onset to the peak of agricultural drought, which is essential in developing a robust drought metric for the Philippines.
ESTIMATION OF DAILY GLOBAL SOLAR IRRADIANCE FROM HIMAWARI-8 PRODUCTS OVER CHINA
Authors: Zhang Y. , Chen L. , Liu J.
Published: May 2022

Abstract. Daily global surface solar irradiance (SSI) is of great importance parameter in the surface energy balance, climate modelling, and solar energy utilization. However, it is still challenging to extend the estimated instantaneous solar radiation to the daily global SSI from either geostationary satellites or polar-orbiting satellites. In this study, a method for estimating the daily global SSI is proposed based on the Himawari-8 hourly SSI products using pixel-by-pixel Gaussian fitting to simulate the diurnal SSI variation. Compared with ground-based observations, the daily global SSI estimated by Gaussian fitting have higher accuracy under various weather conditions than by the simple accumulation or quadratic polynomials, and the coefficient of determination (R2) between the estimated and observed values exceeds 0.86. The verification results also show different estimation accuracies under different weather conditions. The root-mean-square errors (RMSEs) under clear-sky and all-sky conditions are 2.36 MJ/m2 and 3.06 MJ/m2, respectively. In addition, experimental results show that the daily global SSI in China has high spatial heterogeneity. For higherelevation areas with low cloud cover, such as the Qinghai-Tibet Plateau, Inner Mongolia, and Northwest China, the daily global SSI is higher than other areas. In contrast, the values of daily global SSI is relatively low in the eastern regions of Southwest and Northeast China.
ENHANCED SUPER RESOLUTION FOR REMOTE SENSING IMAGERIES
Authors: Kapilaratne R. G. C. J. , Kakuta S. , Kaneta S.
Published: May 2022

Abstract. Single image super resolution (SISR) technology has been attracted much attention from remote sensing community due to its proven potentials in remote sensing applications. Existing SISR techniques varying from conventional interpolation methods to different network architectures. Generative adversarial networks (GANs) are one of the latest network architectures proven a greater potential as a SISR method whereas least attention has been given by the remote sensing community. Several studies have already been carried out on this context. However, yet there is no generalized GAN based approach to super resolve remote sensing imageries. Therefore, this study investigated the potentials of enhanced super resolution generative adversarial (ESRGAN) model to super resolve very high to medium resolution images from high to coarse resolution images for remote sensing applications. Two models were trained and Worldview-3 (WV3) images used as for very high resolution images. Whereas, down sampled WV3 and Sentinel-2(S2) were used as low resolution counterparts. Model performances were qualitatively and quantitatively analysed using standard metrics such as PSNR, SSIM, UIQI, CC, SAM, SID. Evaluation results emphasised super resolved images were preserved the original quality of the satellite images to a greater extent while improving its ground resolution.
A CNN-BASED FLOOD MAPPING APPROACH USING SENTINEL-1 DATA
Authors: Tavus B. , Can R. , Kocaman S.
Published: May 2022

Abstract. The adverse effects of flood events have been increasing in the world due to the increasing occurrence frequency and their severity due to urbanization and the population growth. All weather sensors, such as satellite synthetic aperture radars (SAR) enable the extent detection and magnitude analysis of such events under cloudy atmospheric conditions. Sentinel-1 satellite from European Space Agency (ESA) facilitate such studies thanks to the free distribution, the regular data acquisition scheme and the availability of open source software. However, various difficulties in the visual interpretation and processing exist due to the size and the nature of the SAR data. The supervised machine learning algorithms have increasingly been used for automatic flood extent mapping. However, the use of Convolutional Neural Networks (CNNs) for this purpose is relatively new and requires further investigations. In this study, the U-Net architecture for multi-class segmentation of flooded areas and flooded vegetation was employed by using Sentinel-1 SAR data and altitude information as input. The training data was produced by an automatic thresholding approach using OTSU method in Sardoba, Uzbekistan and Sagaing, Myanmar. The results were validated in Ordu, Turkey and in Ca River, Vietnam by visual comparison with previously produced flood maps. The results show that CNNs have great potential in classifying flooded areas and flooded vegetation even when trained in areas with different geographical setting. The F1 scores obtained in the study for flood and flooded vegetation classes were 0.91 and 0.85, respectively.
QUALITY INSPECTION OF SURFACE DEFORMATION MONITORING RESULTS BASED ON PS-INSAR TECHNOLOGY
Authors: Mao W. , Zhang J. , Li C. , Gao W. , Zhou J. , Xie W. , Xu Y.
Published: May 2022

Abstract. In view of the problems that the permanent scatterer synthetic aperture radar interferometry (PS-InSAR) technology was limited by the difference of processing algorithms and the professional level of data processing personnel, and its monitoring results were still difficult to fully achieve the theoretical accuracy, a new technical process suitable for the detection of PS-InSAR surface deformation monitoring results was proposed in this paper. Based on the analysis of the characteristics of monitoring results, the inspection work was carried out by means of overall inspection. The test parameters, test objects and test items to be tested, as well as the test methods of different test parameters were determined; The key links of PS-InSAR surface deformation monitoring data processing were analyzed in detail, and the technical flow of mathematical accuracy detection was given. Finally, the quality detection of surface deformation monitoring results based on PS-InSAR technology of 3000 square kilometres were carried out, the results showed that the proposed technical route was feasible and quality evaluation result was objective.
MULTI-ATTENTION GHOSTNET FOR DEFORESTATION DETECTION IN THE AMAZON RAINFOREST
Authors: Ortega Adarme M. X. , Costa G. A. O. P. , Feitosa R. Q.
Published: May 2022

Abstract. Efficient deforestation detection techniques are essential to monitor and control illegal logging, thus reducing forest loss and carbon emissions in the Amazon rainforest. Recent works based on Deep Learning (DL) models have been proposed for that purpose. DL-based methods, however, are known to require large amounts of training data to be properly trained. Moreover, the deforestation detection application is characterized by a high class imbalance, as recent deforestation areas usually represent a small fraction of the geographic extents being monitored. In order to produce a lightweight architecture in terms of the number of learnable parameters and address the high class imbalance of the deforestation detection application, we propose a DL model based on the GhostNet architecture, which combines Ghost modules in a fully convolutional architecture. The proposed architecture also includes Spatial Attention Mechanisms attached to the skip connections of the GhostNet in order to better capture the spatial relationships among class features. Experiments were carried out using Sentinel-2 images of a region in the Para´ state, Brazil, in the Amazon rainforest. The results obtained show that the proposed model achieves accuracy levels that are superior to those delivered by state-of-the-art DL architectures, with a lower computational cost due to the smaller number of learnable parameters.
CLASSIFICATION OF VEGETATION CLASSES BY USING TIME SERIES OF SENTINEL-2 IMAGES FOR LARGE SCALE MAPPING IN CAMEROON
Authors: Tagne H. , Le Bris A. , Monkam D. , Mallet C.
Published: May 2022

Abstract. Sentinel-2 satellites provide dense image time series exhibiting high spectral, spatial and temporal resolutions. These images are in particular of utter interest for Land-Cover (LC) mapping at large scales. LC maps can now be computed on a yearly basis at the scale of a country with efficient supervised classifiers, assuming suitable training data are available. However, the efficient exploitation of large amount of Sentinel-2 imagery still remain challenging on unexplored areas where state-of-the-art classifiers are prone to fail. This paper focuses on Land-Cover mapping over Cameroon for the purpose of updating the Very High Resolution national topographic geodatabase. The ι2 framework is adopted and tested for the specificity of the country. Here, experiments focus on generic vegetation classes (five) which enables providing robust focusing masks for higher resolution classifications. Two strategies are compared: (i) a LC map is calculated out of a year long time series and (ii) monthly LC maps are generated and merged into a single yearly map. Satisfactory accuracy scores are obtained (>94% in Overall Accuracy), allowing to provide a first step towards finer-grained map retrieval.
IMPROVING SEMANTIC SEGMENTATION PERFORMANCE BY JOINTLY USING HIGH RESOLUTION REMOTE SENSING IMAGE AND NDSM
Authors: Yang R. , Dai Q. , Cheng H. , Zhang Y. , Chen N. , Wang L.
Published: May 2022

Abstract. Semantic segmentation algorithms based on full convolutional neural network have greatly improved segmentation accuracy of high-resolution remote sensing (RS) images. However, the interpretation of RS images from single sensor is still challenging due to the variety and complexity of land objects, the extreme imbalance distributions of land objects on size and numbers. In contrast, multiple sensors can provide complementary information on the land classes, and thus benefit the interpretation. In this context, this research explores the joint use of RGB optical bands and normalized DSM (nDSM) to analyze an urban scene. The method firstly concatenated three channels RGB image and one channel nDSM band into a four-channel image. Thereafter, ResNet-101 network with fine adjustment were utilized as the backbone network to retain multiple feature information by residual blocks. Then the augmented RGB and nDSM images were used to training the network. The established model was evaluated on the Postdam test set. Results show that the proposed method achieves 86.85% on Overall Accuracy (OA), 77.42% Mean Intersection Over Union (MIOU), which is 6.88% and 11.39% higher than the result achieved by single RGB images. Especially, small targets, such as car and tree, are higher. The experimental results show that the simple structure adjustment of ResNet-101 network can achieve good segmentation performance on RS images (especially small targets) after the combination of twice augmented RGB channels and nDSM channels respectively. In addition, with the addition of nDSM, the accuracy of buildings and trees with height information has been improved.
LONG-TERM LAND SURFACE WATER MONITORING IN THE YELLOW RIVER BASIN OF CHINA BASED ON LANDSAT IMAGERY ON THE GOOGLE EARTH ENGINE
Authors: Cao H. , Han L. , Li L.
Published: May 2022

Abstract. Land surface water is the most active part of the earth's surface layer and plays an important role in material balance and energy transformation. The monitoring of dynamic changes in surface open water helps to grasp the distribution and changes of water resources, which is important for understanding the impact of human activities on water resources and safeguarding the ecological security of the basin. In the study, 72,520 Landsat images were processed using the Google Earth Engine (GEE) cloud platform to study the long-term changes of the open surface water in the Yellow River Basin (YRB) from 2000 to 2020. The results show that (1) The multi-index water body extraction rules (MIWER) is suitable for long-term and large-scale surface water mapping based on Landsat images; (2) From 2000 to 2020, both permanent and seasonal water in the whole YRB and sub-basins have increased. In general, the total open surface water in the YRB show an increasing trend with obvious spatial heterogeneity; (3) The changes of open surface water in the YRB are related to climate change and intensive human activities, but the influencing factors vary in different regions and need to be further studied in the future. The findings of this study can be used to help the policy-makers understand changing water resources and their driving mechanism and provide a reference for water resources management, agricultural irrigation, and ecological protection.
INTRODUCING THE GLOBAL MAPPING OF FLOOD DYNAMICS USING GNSS-REFLECTOMETRY AND THE CYGNSS MISSION
Authors: Zeiger P. , Frappart F. , Darrozes J.
Published: May 2022

Abstract. This study uses the observations from the Cyclone GNSS (CYGNSS) mission to analyze their potential for a global mapping of the floods dynamics in the pan-tropical area using Global Navigation Satellite System (GNSS) Reflectometry (GNSS-R). We base our analysis on the coherent reflectivity derived from CYGNSS observations. We show that the CYGNSS mission configuration allows a gridding at a spatial resolution of 0.1° (∼11 km at the equator), with a time sampling of 1 week. We calculate the average and standard deviation values of reflectivity in the grid pixels at each time step. A Gaussian weighted window of one month is used to fill the gaps which appear in the time series due to the pseudo-random sampling of CYGNSS observations. The maps of these two parameters are then compared to elevation data from the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), to Land Cover information from the European Space Agency’s (ESA) Climate Change Initiative (CCI), and to a reference set of static inundation maps. We observe a strong correspondence between CYGNSS reflectivity-based parameters, and the percentage of flooded areas established in the literature. The detection of the major floodplains, irrigated crops, open water areas, and the hydrological network using CYGNSS data is clear. We observe some limitations over the areas with high elevation – due to the CYGNSS mission specificities – and over the most densely vegetated areas. At some point it could prevent the correct extraction of flood patterns. For a future complete CYGNSS-based flood product, the integration of ancillary data describing the major role of land cover, biomass and topography on the GNSS-R returned signals should be necessary to extract the correct features of water cycle.
A COGNITIVE APPROACH FOR LANDSYSTEM IDENTIFICATION USING A GRAPH DATABASE – TOWARDS THE IDENTIFICATION OF LANDFORMS IN CONTEXT
Authors: Ramiaramanana H. , Guilbert E. , Moulin B.
Published: May 2022

Abstract. A landform is any physical feature of the earth's surface having a characteristic, recognizable shape. Most landform identification methods rely on OBIA (Object-Based Image Analysis) techniques to segment the terrain data and classify segments into objects that are assumed to compose the landform. However, geomorphologists can visually recognize any landform, considering the characteristics of the surrounding environment that plays the role of context. This notion of context was not considered in previous landform identification methods. We propose to model it using the notion of landsystem. Landsystems are geomorphologic elements that result from a set of natural geomorphological processes. They are also easily recognized by geomorphologists. In this paper, we present a new knowledge-based method to automatically identify landsystems as the context for landform identification. We first present a conceptual model as a core ontology of geomorphologic elements including landsystems and landforms, capturing relevant geomorphologists’ knowledge. Then, we present how this model is extended to create a domain ontology for a chosen domain in geomorphology. We illustrate such an extension for the case of mountainous glacial valleys. We used the graph database engine Neo4J to implement the domain ontology and to develop a knowledge-based system (a framework) to automatically identify landsystems from spatial datasets. We present the architecture of our framework and discuss how it is used to support: 1) the knowledge acquisition tasks; 2) the spatial data preparation task; 3) the processing of the user’s request seeking landsystems in a chosen study area.
CHARACTERISTICS COGNITION OF TYPICAL SURFACE GEOHAZARDS SCENE IN MINING AREAS AND REPRESENTATION OF GEO-INFOGRAPHICS SPECTRUM
Authors: Zhang J.
Published: May 2022

Abstract. The concept of surface geohazards in mining area is put forward, and the surface geohazards in mining area are divided into three categories: first is surface subsidence fissures, collapse, second is instability of structures, slope deformation hazards (high and steep slopes, landslides and collapses) , debris flows (debris flows, tailings reservoir dam break), third is vegetation cover degradation and environmental and ecological damage in mining area. Author analyses the research status of spatio-temporal characteristics of surface geohazards in mining areas and the representation of geo-infographics Spectrum. Characteristics of typical surface geohazards scene in mining areas can be recognized using multi-resource spatial data, adopting abstract generalization, visualization and analysis of the various features and phenomena from four aspects: geographical environment, geological environment, engineering activity and geohazards. The field presentations of spatial morphological structure and geo-environmental conditions and spatio-temporal process in surface geohazards characteristic area are summarized as data fields, which express the shape, structure, location and distribution of geohazard bodies, action and physical fields which is all fields affected the data field and the coupled field which is scalar or vector field formed by the coupling of two or more fields. By using spatial location, semantic description, attribute characteristics, geometric shape, evolution process and the information of the relationship between features, taking into account the characteristics of object structure, distribution and coupling, the geohazard infographics spectrum is constructed and characterize the multi-field geo-infographics spectrum of surface geohazards in mining areas. It is a composite analysis method of time and space. A preliminary example of geo-infographics spectrum is given.
STACKING-BASED UNCERTAINTY MODELLING OF STATISTICAL AND MACHINE LEARNING METHODS FOR RESIDENTIAL PROPERTY VALUATION
Authors: Jafari A. , Delavar M. R. , Stein A.
Published: May 2022

Abstract. Estimating real estate prices helps to adapt informed policies to regulate the real estate market and assist sellers and buyers to have a fair business. This study aims to estimate the price of residential properties in District 5 of Tehran, Capital of Iran, and model its associated uncertainty. The study implements the Stacking technique to model uncertainties by integrating the outputs of basic models. Basic models must have a good performance for their combinations to have acceptable results. This study employs four statistical and machine learning models as basic models: Random Forest (RF), Ordinary Least Squares (OLS), Weighted K-Nearest Neighbour (WKNN), and Support Vector Regression (SVR) to estimate the price of residential properties. The results show that the integrated output is more accurate for the quadruple combination mode than for any of the binary and triple combinations of the basic models. Comparing the Stacking technique with the Voting technique, it is shown that the Mean Absolute Percentage Error (MAPE) reduces from 10.18% to 9.81%. Hence we conclude that our method performs better than the Voting technique.
LAND SUBSIDENCE PREDICTION THROUGH MODELING OF TEMPORAL ATTRIBUTE PREDICTION OF KNOWLEDGE GRAPH
Authors: Lei X. , Song W. , Fan R. , Feng R. , Wang L.
Published: May 2022

Abstract. Land subsidence is a geological disaster. It will lead to the decline of land elevation, resulting in the potential safety hazards of urban facilities. Thus, the prediction of land subsidence displacement is significant. Among the existing prediction methods, the methods based on the time-series prediction model only analyze the settlement series data without considering the settlement mechanism, so they are easy to apply. However, they less consider the influence of other factors on land subsidence. Besides, they independently input displacement time-series data from different monitoring points without considering their relationship. To solve these problems, we take the monitoring point as the entity and take the DEM, soil type, building height, and land subsidence displacement sequence at the corresponding position of the monitoring point as the attributes to construct the knowledge graph. And then, we propose a framework Graph-TAP for modeling temporal attribute prediction of the knowledge graph’s entity. This framework learns the representation of events with the specific entity at first. Then it captures the temporal dependency between historical events using GRU. Finally, it predicts the entity’s displacement attribute. We randomly selected 61 subsidence monitoring points in Shenzhen, China. We used the land subsidence displacement InSAR time-series data (12-day time interval) and other attribute data from June 22, 2015, to April 5, 2016, for model training, validation, and testing. The experimental results show that our method is better than the time-series prediction based on the LSTM model and the DARNET (a knowledge graph temporal attribute prediction framework).
Earthquake Building Damage Detection based on Synthetic Aperture Radar Imagery and Machine Learning
Authors: Rao Anirudh , Jung Jungkyo , Silva Vitor , Molinario Giuseppe , Yun Sang-Ho
Published: May 2022

Abstract. This article presents a framework for semi-automated building damage assessment due to earthquakes from remote sensing data and other supplementary datasets, while also leveraging recent advances in machine-learning algorithms. The framework integrates high-resolution building inventory data with earthquake ground shaking intensity maps and surface-level changes detected by comparing pre- and post-event InSAR (interferometric synthetic aperture radar) images. We demonstrate the use of ensemble models in a machine-learning approach to classify the damage state of buildings in the area affected by an earthquake. Both multi-class and binary damage classification are attempted for four recent earthquakes and we compare the predicted damage labels with ground truth damage grade labels reported in field surveys. For three out of the four earthquakes studied, the model is able to identify over fifty percent or nearly half of the damaged buildings successfully when using binary classification. Multi-class damage grade classification using InSAR data has rarely been attempted previously, and the case studies presented in this report represent one of the first such attempts using InSAR data.
Characterising the interaction of tropical and extratropical air masses controlling East Asian summer monsoon progression using a novel frontal detection approach
Authors: Volonté Ambrogio , Turner Andrew G. , Schiemann Reinhard , Vidale Pier Luigi , Klingaman Nicholas P.
Published: May 2022

Abstract. The East Asian summer monsoon (EASM) is a complex phenomenon, influenced by both tropical and mid-latitude dynamics and by the presence of the Tibetan Plateau. The EASM front (EASMF) separates tropical and extratropical air masses as the monsoon marches northwards. Although the different factors behind EASM progression are illustrated in a number of studies, their interactions, in particular between tropical and extratropical air masses, still need to be clarified. In this study we apply Eulerian and Lagrangian methods to the ERA5 reanalysis dataset to provide a comprehensive study of the seasonal progression of and interannual variability in the EASM, and we highlight the dynamics of the air masses converging at its front. A frontal detection algorithm is used to perform a front-centred analysis of EASM progression. The analysis highlights the primary role of the subtropical westerly jet (STWJ) and of the western North Pacific subtropical high (WNPSH) in controlling the strength and the poleward progression of the EASMF, in particular during Mei Yu, the primary stage of EASM progression. These forcings act to steer the southerly advection of low-level moist tropical air, modulated by the seasonal cycle of the Asian monsoon. The Mei Yu stage is distinguished by an especially clear interaction between tropical and extratropical air masses converging at the EASMF. The analysis of composites based on the latitude of the EASMF during Mei Yu reveals the influence exerted by the STWJ on the cool extratropical flow impacting on the northern side of the EASMF, whose progression is also dependent on the location of the WNPSH. In turn, this affects the extent of the warm moist advection on its southern side and the distribution and intensity of resultant rainfall over China. This study shows the validity of an analysis of EASM progression focused on its front and on the related low- and mid-level airstreams, at least in the Mei Yu stage. The framework highlighted shows how the regional flow over East Asia drives the low-level airstreams that converge at the EASMF, thus controlling the shape of EASM progression. This framework provides a basis for studies of climate variability and extreme events and for model evaluation.
A Pilot Study of Reducing Test Anxiety in a Cohort of Underrepresented in Medicine MCAT Students Using Near-Peer Coaching
Authors: Liu Benjamin , Hodge Allen , Jushka Crystal , Hueston William J
Published: May 2022

Background: The Medical College Admission Test (MCAT) is a standardized exam taken by prospective medical students. The MCAT is critical for success in most of the US and parts of Canada, and such pressures may enhance test anxiety. For students from Underrepresented in Medicine (URM) backgrounds, this is often compounded by being the first in their family to take the MCAT.
Methods: We conducted a literature review for interventions on test-related anxiety. Based on our findings, we elected to establish a pilot near-peer coaching (NPC) program for URM students enrolled at the Medical College of Wisconsin MCAT program. We quantified baseline and specific time point test-anxieties using the validated Westside test anxiety scale. We asked about MCAT concerns and program impressions via a free-response section and analyzed results with inductive analysis.
Results: Our review could find no other studies examining MCAT related test anxiety in the prospective medical student population. NPC was chosen because of its accessibility. At baseline, approximately 50% of students had at least moderately high-test anxiety; meeting the threshold for intervention. Most students perceived themselves as unconfident in their ability to do well on the MCAT. We observed a decrease in test anxiety after coaching sessions. Students received the program well however, wanted to be able to choose the content and have more meetings.
Conclusion: This observational pilot study suggests that URM pre-medical students have MCAT related test anxieties high enough to warrant intervention and that NPC is well-received and correlates to reduced test anxiety levels.