Applied Computing and Geosciences最新文献

{"title":"Advancing geological image segmentation: Deep learning approaches for rock type identification and classification","authors":"Amit Kumar Gupta ,&nbsp;Priya Mathur ,&nbsp;Farhan Sheth ,&nbsp;Carlos M. Travieso-Gonzalez ,&nbsp;Sandeep Chaurasia","doi":"10.1016/j.acags.2024.100192","DOIUrl":"10.1016/j.acags.2024.100192","url":null,"abstract":"<div><p>This study aims to tackle the obstacles linked with geological image segmentation by employing sophisticated deep learning techniques. Geological formations, characterized by diverse forms, sizes, textures, and colors, present a complex landscape for traditional image processing techniques. Drawing inspiration from recent advancements in image segmentation, particularly in medical imaging and object recognition, this research proposed a comprehensive methodology tailored to the specific requirements of geological image datasets. To establish the dataset, a minimum of 50 images per rock type was deemed essential, with the majority captured at the University of Las Palmas de Gran Canaria and during a field expedition to La Isla de La Palma, Spain. This dual-source approach ensures diversity in geological formations, enriching the dataset with a comprehensive range of visual characteristics. The study involves the identification of 19 distinct rock types, each documented with 50 samples, resulting in a comprehensive database containing 950 images. The methodology involves two crucial phases: initial preprocessing of the dataset, focusing on formatting and optimization, and subsequent application of deep learning models—ResNets, Inception V3, DenseNets, MobileNets V3, and EfficientNet V2 large. Preparing the dataset is crucial for improving both the quality and relevance, thereby to ensure the optimal performance of deep learning models, the dataset was preprocessed. Following this, transfer learning or more specifically fine-tuning is applied in the subsequent phase with ResNets, Inception V3, DenseNets, MobileNets V3, and EfficientNet V2 large, leveraging pre-trained models to enhance classification task performance. After fine-tuning eight deep learning models with optimal hyperparameters, including ResNet101, ResNet152, Inception-v3, DenseNet169, DenseNet201, MobileNet-v3-small, MobileNet-v3-large, and EfficientNet-v2-large, comprehensive evaluation revealed exceptional performance metrics. DenseNet201 and InceptionV3 attained the highest accuracy of 98.49% when tested on the original dataset, leading in precision, sensitivity, specificity, and F-score. Incorporating preprocessing steps further improved results, with all models exceeding 97.5% accuracy on the preprocessed dataset. In K-Fold cross-validation (k = 5), MobileNet V3 large excelled with the highest accuracy of 99.15%, followed by ResNet101 at 99.08%. Despite varying training times, models on the preprocessed dataset showed faster convergence without overfitting. Minimal misclassifications were observed, mainly among specific classes. Overall, the study's methodologies yielded remarkable results, surpassing 99% accuracy on the preprocessed dataset and in K-Fold cross-validation, affirming the efficacy in advancing rock type understanding.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100192"},"PeriodicalIF":2.6,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000399/pdfft?md5=a986940f5d719d111fdfe4229e223af6&pid=1-s2.0-S2590197424000399-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142158095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretation techniques to explain the output of a spatial land subsidence hazard model in an area with a diverted tributary","authors":"Razieh Seihani ,&nbsp;Hamid Gholami ,&nbsp;Yahya Esmaeilpour ,&nbsp;Alireza Kamali ,&nbsp;Maryam Zareh","doi":"10.1016/j.acags.2024.100191","DOIUrl":"10.1016/j.acags.2024.100191","url":null,"abstract":"<div><p>Due to the nature of black-box machine learning (ML) models used in the spatial modelling field of environmental and natural hazards, the interpretation of predictive model outputs is necessary. For this purpose, we applied four interpretation techniques consisting of interaction plot, permutation feature importance (PFI) measure, shapley additive explanation (SHAP) decision plot, and accumulated local effects (ALE) plot to explain and interpret the output of an ML model applied to map land subsidence (LS) in the Nazdasht plain, Hormozgan province, southern Iran. We applied a stepwise regression (SR) algorithm and five ML models (Cforest (as a conditional random forest), generalized linear model (GLM), multivariate linear regression (MLR), partial least squares (PLS) and extreme gradient boosting (XGBoost)) to select important features and to map the LS hazard, respectively. Thereafter, several interpretation techniques were used to explain the spatial ML hazard model output. Our findings revealed that a GLM model was the most accurate approach to map LS in our study area, and that 24.3% of the total study area had a very high susceptibility to the LS hazard. According to the interpretation techniques, land use, elevation, groundwater level and vegetation were the most important variables controlling the LS hazard and also the most important variables contributing to the model’s output. Overall, human activities, especially the diversion of the route of one of the main tributaries feeding the plain and the recharging of groundwater five decades ago, intensified the current LS occurrence. Therefore, management activities such as water spreading projects upstream of the plain can be useful to mitigate LS occurrence in the plain.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100191"},"PeriodicalIF":2.6,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000387/pdfft?md5=aff9aab3e9da8297a983487d668498f5&pid=1-s2.0-S2590197424000387-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142088569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved reservoir characterization of thin beds by advanced deep learning approach","authors":"Umar Manzoor ,&nbsp;Muhsan Ehsan ,&nbsp;Muyyassar Hussain ,&nbsp;Yasir Bashir","doi":"10.1016/j.acags.2024.100188","DOIUrl":"10.1016/j.acags.2024.100188","url":null,"abstract":"<div><p>Targeting reservoirs below seismic resolution presents a major challenge in reservoir characterization. High-resolution seismic data is critical for imaging the thin gas-bearing Khadro sand facies in several fields within the Lower Indus Basin (LIB). To truly characterize thin beds below tuning thickness, we showcase an optimally developed deep learning technique that can save up to 75% turn-around time while significantly reducing cost. Our workflow generates high-frequency acoustic impedance synthetics by utilizing a deep neural network (DNN) at the reservoir level vis-a-vis validating the results with existing geological facies. Simultaneously, we introduce continuous wavelet transform (CWT); wherein the three components (real, imaginary, and magnitude) are interrelated to obtain a resultant high-frequency seismic volume. A strong agreement is established at available wells to achieve a higher resolution seismic by injecting higher frequencies, which is then populated throughout the 3D cube. An excellent correlation is met with key seismic attributes extracted across the field for original and CWT-based synthetic seismic. The augmented seismic volume with enhanced frequency range substantiates the dominant frequency (F_d) and resolves thin beds, which is also validated with the help of wedge modeling of both acquired and high-frequency datasets. As a geologically valid solution, our approach effectively resolves an initially 54 m bed to ∼25 m. This deep-learning methodology is ideally suited to regions where the acquired seismic has limited resolution and lacks advanced reservoir characterization.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100188"},"PeriodicalIF":2.6,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000351/pdfft?md5=80034ccf54e0197dfeb31abc6927a92f&pid=1-s2.0-S2590197424000351-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142088568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A supervised machine learning procedure for EPMA classification and plotting of mineral groups","authors":"R. Cossio ,&nbsp;S. Ghignone ,&nbsp;A. Borghi ,&nbsp;A. Corno ,&nbsp;G. Vaggelli","doi":"10.1016/j.acags.2024.100186","DOIUrl":"10.1016/j.acags.2024.100186","url":null,"abstract":"<div><p>An analytical method to automatically characterize rock samples for geological or petrological purposes is here proposed, by applying machine learning approach (ML) as a protocol for saving experimental times and costs.</p><p>Proper machine learning algorithms, applied to automatically acquired microanalytical data (i.e., Electron Probe Micro Analysis, EPMA), carried out with a SEM-EDS microprobe on randomly selected areas from a petrographic polished thin section, are trained, used, tested, and reported.</p><p>Learning and Validation phases are developed with literature mineral databases of electron microprobe analyses on 15 main rock-forming mineral groups. The Prediction phase is tested using an eclogite rock from the Western Alps, considered as an unknown sample: randomly selected areas are acquired as backscattered images whose intervals of gray levels, appropriately set in the gray level histogram, allow the automated particle mineral separation: automated separating Oxford Instruments Aztec Feature ® packages and a mineral plotting software are applied for mineral particle separation, crystal chemical formula calculation and plotting.</p><p>Finally, a microanalytical analysis is performed on each separated mineral particle. The crystal chemical formula is calculated, and the final classification plots are automatically produced for any determined mineral. The final results show good accuracy and analytical ease and assess the proper nature of the unknown eclogite rock sample. Therefore, the proposed analytical protocol is especially recommended in those scenarios where a large flow of microanalytical data is automatically acquired and needs to be processed.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100186"},"PeriodicalIF":2.6,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000338/pdfft?md5=5f9a7ff05910f5e248a1bc9ca4b633a6&pid=1-s2.0-S2590197424000338-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LSTM-based DEM generation in riverine environment","authors":"Virág Lovász ,&nbsp;Ákos Halmai","doi":"10.1016/j.acags.2024.100187","DOIUrl":"10.1016/j.acags.2024.100187","url":null,"abstract":"<div><p>In the broad field of sensors and 3D information retrieval, bathymetric reconstruction from side-scan sonar imaging is associated with unique technical hurdles. Neural Networks have recently led to promising new solutions in this field, but the available methods tend to be complex and data-intensive in a way typically making their use in a riverine environment impossible. Throughout our work, we have focused on simplifying the problem-handling and treating compatibility with a riverine environment as priority. In our work, Long Short-Term Memory proved to be effective in a surprisingly simple form. Combined with traditional post-processing techniques in the GIS environment, like median filtered focal statistics, our workflow ultimately results in ∼0.259 m median of error on the evaluation dataset of the Dráva River.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100187"},"PeriodicalIF":2.6,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S259019742400034X/pdfft?md5=17b4129af31ed050fc8151abebd2cdbf&pid=1-s2.0-S259019742400034X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142011680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-term temperature prediction with hybrid autoencoder algorithms","authors":"J. Pérez-Aracil ,&nbsp;D. Fister ,&nbsp;C.M. Marina ,&nbsp;C. Peláez-Rodríguez ,&nbsp;L. Cornejo-Bueno ,&nbsp;P.A. Gutiérrez ,&nbsp;M. Giuliani ,&nbsp;A. Castelleti ,&nbsp;S. Salcedo-Sanz","doi":"10.1016/j.acags.2024.100185","DOIUrl":"10.1016/j.acags.2024.100185","url":null,"abstract":"<div><p>This paper proposes two hybrid approaches based on Autoencoders (AEs) for long-term temperature prediction. The first algorithm comprises an AE trained to learn temperature patterns, which is then linked to a second AE, used to detect possible anomalies and provide a final temperature prediction. The second proposed approach involves training an AE and then using the resulting latent space as input of a neural network, which will provide the final prediction output. Both approaches are tested in long-term air temperature prediction in European cities: seven European locations where major heat waves occurred have been considered. The long-term temperature prediction for the entire year of the heatwave events has been analysed. Results show that the proposed approaches can obtain accurate long-term (up to 4 weeks) temperature prediction, improving Persistence and Climatology in the benchmark models compared. In heatwave periods, where the persistence of the temperature is extremely high, our approach beat the persistence operator in three locations and works similarly in the rest of the cases, showing the potential of this AE-based method for long-term temperature prediction.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100185"},"PeriodicalIF":2.6,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000326/pdfft?md5=5456efe65b92894adbcaf61c5ff34ab1&pid=1-s2.0-S2590197424000326-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An in-depth multivariate analysis of PM2.5 concentration and associated premature deaths in Europe and its strategic relationship with sustainability","authors":"Laura Sanz-Martín,&nbsp;Javier Parra-Domínguez,&nbsp;Juan Manuel Corchado","doi":"10.1016/j.acags.2024.100184","DOIUrl":"10.1016/j.acags.2024.100184","url":null,"abstract":"<div><p>The strategic importance of sustainability is evident when it comes, for example, to health. Public policies aimed at mitigating the effects of harmful substances, such as fine particulate matter (PM 2.5), are justified by the direct link between fine particulate matter and the health of citizens, in this case, premature deaths. An advanced statistical and exhaustive analysis of different areas and countries shows a strong link between exposure to <span><math><mrow><mi>P</mi><msub><mrow><mi>M</mi></mrow><mrow><mn>2</mn><mo>.</mo><mn>5</mn></mrow></msub></mrow></math></span>, premature deaths in other countries, and significant differences in <span><math><mrow><mi>P</mi><msub><mrow><mi>M</mi></mrow><mrow><mn>2</mn><mo>.</mo><mn>5</mn></mrow></msub></mrow></math></span> levels between urban and rural areas.</p><p>Although <span><math><mrow><mi>P</mi><msub><mrow><mi>M</mi></mrow><mrow><mn>2</mn><mo>.</mo><mn>5</mn></mrow></msub></mrow></math></span> concentration has decreased in most countries studied, this effort must be continued and aligned with the Sustainable Development Goals of the 2030 Agenda, underlining the need to implement effective air pollution control policies to reduce the health risks associated with <span><math><mrow><mi>P</mi><msub><mrow><mi>M</mi></mrow><mrow><mn>2</mn><mo>.</mo><mn>5</mn></mrow></msub></mrow></math></span> exposure. To this end, identifying temporal trends and geographical patterns can guide the development of specific interventions tailored to the needs of each region.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100184"},"PeriodicalIF":2.6,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000314/pdfft?md5=44bb998cf497f5fc1513d6e69d4c4f26&pid=1-s2.0-S2590197424000314-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flood susceptibility mapping: Integrating machine learning and GIS for enhanced risk assessment","authors":"Zelalem Demissie ,&nbsp;Prashant Rimal ,&nbsp;Wondwosen M. Seyoum ,&nbsp;Atri Dutta ,&nbsp;Glen Rimmington","doi":"10.1016/j.acags.2024.100183","DOIUrl":"10.1016/j.acags.2024.100183","url":null,"abstract":"<div><p>Flooding presents a formidable challenge in the United States, endangering lives and causing substantial economic damage, averaging around $5 billion annually. Addressing this issue and improving community resilience is imperative. This project employed machine learning techniques and publicly available data to explore the factors influencing flooding and to develop flood susceptibility maps at various spatial resolutions. Six machine learning algorithms, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), K-nearest neighbor (KNN), Adaptive Boosting (Ada Boost), and Extreme Gradient Boosting (XGB) were used. Geospatial datasets comprising thirteen predictor variables and 1528 flood inventory data collected since 1996 were analyzed. The predictor variables are rainfall, elevation, slope, aspect, flow direction, flow accumulation, Topographic Wetness Index (TWI), distance from the nearest stream, evapotranspiration, land cover, impervious surface, land surface temperature, and hydrologic soil group. Five hundred twenty-eight non-flood data points were randomly created using a stream buffer for two scenarios. A total of 2964 data points were classified into flooded (1) and non-flooded (0) categories and used as a target. Overall, testing results showed that the XGB and RF models performed relatively well in both cases over multiple resolutions compared to other models, with an accuracy ranging from 0.82 to 0.97. Variable importance analysis depicted that predictor variables such as distance from the streams, hydrologic soil type, rainfall, elevation, and impervious surfaces significantly affected flood prediction, suggesting a strong association with the underlying driving process. The improved performance and the variation of the susceptible areas across two scenarios showed that considering predictor variables with multiple resolutions and appropriate non-flooding training points is critical for developing flood-susceptibility models. Furthermore, using tree-based ensemble algorithms like RF and XG boost in the stack generalization approach can help achieve robustness in a flood susceptibility model where multiple algorithms are being evaluated.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100183"},"PeriodicalIF":2.6,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000302/pdfft?md5=9e61c017b8afc6f574d15d4606f34de9&pid=1-s2.0-S2590197424000302-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141953181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling river flow for flood forecasting: A case study on the Ter river","authors":"Fabián Serrano-López ,&nbsp;Sergi Ger-Roca ,&nbsp;Maria Salamó ,&nbsp;Jerónimo Hernández-González","doi":"10.1016/j.acags.2024.100181","DOIUrl":"10.1016/j.acags.2024.100181","url":null,"abstract":"<div><p>Floods affect chronically many communities around the world. Their socioeconomic impact increases year-by-year, boosted by global warming and climate change. Combined with long-term preemptive measures, preparatory actions are crucial when floods are imminent. In the last decade, machine learning models have been used to anticipate these hazards. In this work, we model the Ter river (NE Spain), which has historically suffered from floods, using traditional ML models such as K-nearest neighbors, Random forests, XGBoost and Linear regressors. Publicly available river flow and precipitation data was collected from year 2009 to 2021. Our analysis measures the time elapsed between observing a flow rise event at different stations (or heavy rain, for rainfall stations), and use the measured time lags to align the data from the different stations. This information provides increased interpretability to our river flow models and flood forecasters. A thorough evaluation reveals that ML techniques can be used to make short-term predictions of the river flow, even during heavy rain and large flow rise events. Moreover, our flood forecasting system provides valuable interpretable information for setting up timely preparatory actions.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100181"},"PeriodicalIF":2.6,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000284/pdfft?md5=68aa83f28d78fe7b1a8b02573085aedf&pid=1-s2.0-S2590197424000284-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141839881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new approach to dust source mapping using visual interpretation and object-oriented segmentation of satellite imagery","authors":"Ali Darvishi Boloorani ,&nbsp;Nastaran Nasiri ,&nbsp;Masoud Soleimani ,&nbsp;Ramin Papi ,&nbsp;Fatemeh Amiri ,&nbsp;Najmeh Neysani Samany ,&nbsp;Azher Ibrahim Al-Taei ,&nbsp;Saham Mirzaei ,&nbsp;Ali Al-Hemoud","doi":"10.1016/j.acags.2024.100182","DOIUrl":"10.1016/j.acags.2024.100182","url":null,"abstract":"<div><p>The emission of dust particles, mainly from arid and semi-arid lands, as a result of climate change and human activities, is known to be a global issue. Identifying dust emission sources is the first key step in dealing with the hazardous consequences of this rising phenomenon. This study is an attempt to address one of the major challenges in mapping dust emission sources. Accordingly, an innovative approach based on visual interpretation of multi-temporal MODIS-Terra/Aqua imagery and object-oriented image segmentation techniques has been developed and implemented in the study areas of the Tigris and Euphrates basin and eastern Iran. This approach takes advantage of land surface characteristics (i.e., dust drivers), including geomorphology, soil, land use/cover, and land surface radiation, to attribute dust emission hotspots to their corresponding areas using multi-source remote sensing data. The results show that the multi-resolution segmentation algorithm with optimized parameters can identify homogeneous segments corresponding to dust emission sources in the study areas with an average spatial agreement of ∼92% compared to the reference areas. Our findings emphasize the robustness and generalizability of the proposed approach, and its capabilities can be used in a complementary way with visual interpretation of satellite images to map dust sources with high spatial accuracy.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100182"},"PeriodicalIF":2.6,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000296/pdfft?md5=0b358b93723ad5c50c855916090d135f&pid=1-s2.0-S2590197424000296-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141850411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}