Preprints

Filtering by Subject: Computer Sciences

Global drivers of forest loss at 1 km resolution

Michelle Sims, Radost Stanimirova, Anton Raichuk, et al.

Published: 2024-12-20
Subjects: Computer Sciences, Environmental Sciences, Geographic Information Sciences, Remote Sensing

Forests are in decline worldwide due to human activities such as agricultural expansion, urbanization, and mineral extraction. Forest loss due to generally temporary causes, such as wildfire and forest management, is important to distinguish from permanent land use conversion due to the differing ecological and climate impacts of these disturbances and for the purposes of developing effective [...]

A Conversational Intelligent Assistant for Enhanced Operational Support in Floodplain Management with Multimodal Data

Vinay Pursnani, Muhammed Yusuf Sermet, Ibrahim Demir

Published: 2024-12-19
Subjects: Artificial Intelligence and Robotics, Civil and Environmental Engineering, Computer Sciences, Databases and Information Systems, Earth Sciences, Environmental Engineering, Environmental Sciences, Environmental Studies, Hydraulic Engineering, Hydrology, Water Resource Management

Floodplain management is crucial for mitigating flood risks and enhancing community resilience, yet floodplain managers often face significant challenges, including the complexity of data analysis, regulatory compliance, and effective communication with diverse stakeholders. This study introduces Floodplain Manager AI, an innovative artificial intelligence (AI) based virtual assistant designed to [...]

Tracking Drought Impacts from Texts: Towards AI-Assisted Drought Impact Detection

Beichen Zhang, Kelly Helm Smith, Frank Schilder, et al.

Published: 2024-12-06
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Environmental Indicators and Impact Assessment, Environmental Sciences, Hydrology

Drought is recognized for its extensive and varied impacts. Based on the drought-related textual datasets from the National Drought Mitigation Center, our research applies advanced artificial intelligence techniques, including deep learning and natural language processing, to enhance the monitoring of multifaceted drought impacts in the United States. This study also delves into predicting [...]

Integration and Execution of Community Land Model Urban (CLMU) in a Containerized Environment

Junjie Yu, Yuan Sun, Sarah Lindley, et al.

Published: 2024-11-27
Subjects: Civil Engineering, Computer Sciences, Earth Sciences, Environmental Engineering, Environmental Sciences

The Community Land Model Urban (CLMU) is a process-based numerical urban climate model that simulates the interactions between the atmosphere and urban surfaces, serving as a powerful tool for the convergence of urban and climate science research. Despite its advanced capabilities, CLMU presents significant challenges for users unfamiliar with numerical modeling due to the complexities of model [...]

Application of machine learning methods to forecast petrophysical properties in basalts of the Serra Geral Group: Implications for carbon storage

João Paulo Guilherme Rodrigues Alves, Claudio Riccomini

Published: 2024-10-22
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Environmental Sciences, Geophysics and Seismology, Oil, Gas, and Energy, Physical Sciences and Mathematics

This study applies machine learning techniques for forecasting petrophysical properties (density, porosity, and permeability) in the basalts of the Serra Geral Group, located in the Paraná Basin, Brazil. These properties are crucial for the successful implementation of carbon capture and storage (CCS), an important technology to combat climate change. Employing machine learning models—XGBoost, [...]

DARTS: Multi-year database of AI-detected retrogressive thaw slumps in the circum-arctic permafrost region

Ingmar Nitze, Konrad Heidler, Nina Nesterova, et al.

Published: 2024-10-20
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Geomorphology, Physical Sciences and Mathematics

Retrogressive Thaw Slumps (RTS) and Active Layer Detachment Slides (ALD) are widespread thermal mass-wasting hillslope failures triggered by thawing permafrost. Despite increasing rates of these failures, knowledge about their pan-arctic spatial and temporal distribution remains limited. We present the Database of AI-detected Arctic RTS and ALD footprints (DARTS), the largest hillslope [...]

Virtual laser scanning of dynamic scenes (VLS-4D): Current approaches and future perspectives in remote sensing

Hannah Weiser, Bernhard Höfle

Published: 2024-10-07
Subjects: Computer Sciences, Earth Sciences, Environmental Sciences, Geographic Information Sciences, Geography, Numerical Analysis and Scientific Computing, Physical and Environmental Geography, Remote Sensing

Virtual laser scanning (VLS) has proven to be a useful tool for survey planning, method development and training data generation in a variety of areas of Earth and environmental sciences. Until recently, most applications have used static representations of the real or a fictive environment, neglecting the inherent dynamics of our world, that also affect Light Detection and Ranging (LiDAR) [...]

Climate Suitability Modelling of Miracle Tree Moringa oleifera Distribution in Pakistan using MaxEnt

Kainat Muniba, Muhammad Naveed Jafar, Muhammad Nawaz Chaudhary, et al.

Published: 2024-09-24
Subjects: Computer Sciences

Climate change has badly affected many countries in the world and Pakistan is being listed among the top ten of those countries. Pakistan is facing many adverse consequences due to climate change, which includes food security issues, water scarcity, temperature rise and high air pollution index. Moringa oleifera, known to be the miracle tree, has multiple advantages and can be used to combat [...]

Comparative Analysis of SVM and CNN for Hyperspectral Image Classification

Md Laraib Salam, Raghvendra Sahai Saxena

Published: 2024-08-13
Subjects: Computer Engineering, Computer Sciences, Other Computer Sciences, Physical Sciences and Mathematics

This paper presents a comparative analysis of tra- ditional machine learning methods and Convolutional Neural Networks (CNNs) for hyperspectral image classification. Utilizing the Indian Pines dataset, we explore the efficacy of Principal Component Analysis (PCA) combined with a Support Vector Machine (SVM) classifier against a deep learning approach involving CNNs. Our methodology includes [...]

TROPICAL STORM SURGE: FORMATION, IMPACT, AND RECENT ADVANCES IN ITS PREDICTION TOWARDS DEVELOPING MITIGATION STRATEGIES

Karinja Thejaswi

Published: 2024-07-19
Subjects: Computer Sciences, Earth Sciences, Environmental Sciences, Oceanography and Atmospheric Sciences and Meteorology, Other Physical Sciences and Mathematics, Physics, Planetary Sciences

Tropical storm surge poses significant risks to coastal areas, necessitating precise prediction for effective emergency preparedness and mitigation. Recent advances in numerical models such as SLOSH, ADCIRC, and FVCOM have revolutionized storm surge forecasting by accurately simulating complex hydrodynamic processes, bolstered by ADCIRC's use of high-resolution grids and parallel computing for [...]

A Review of Machine Learning in Snow Water Equivalent Monitoring

Faye Hsu, Ziheng Sun, Gokul Prathin, et al.

Published: 2024-05-09
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Databases and Information Systems, Earth Sciences, Hydrology, Numerical Analysis and Scientific Computing, Oceanography and Atmospheric Sciences and Meteorology

In recent years, the scientific community focused on snow dynamics has witnessed a surge in efforts aimed at enhancing Snow Water Equivalent (SWE) monitoring capabilities, largely propelled by the incorporation of Machine Learning (ML) techniques. This comprehensive review delves into the current state of research within this evolving domain, shedding light on the indispensable role of precise [...]

GeoAI and the Future of Spatial Analytics

Wenwen Li, Samantha T. Arundel

Published: 2024-05-01
Subjects: Computer Sciences, Earth Sciences, Environmental Sciences, Environmental Studies, Geography, Library and Information Science, Physical Sciences and Mathematics, Social and Behavioral Sciences

This chapter discusses the challenges of traditional spatial analytical methods in their limited capacity to handle big and messy data, as well as mining unknown or latent patterns. It then introduces a new form of spatial analytics – geospatial artificial intelligence (GeoAI) - and describes the advantages of this new strategy in big data analytics and data-driven discovery. Finally, a [...]

Towards an Open and Integrated Cyberinfrastructure for River Morphology Research in the Big Data Era

Venkatesh Merwade, Ibrahim Demir, Marian Muste, et al.

Published: 2024-02-16
Subjects: Computer and Systems Architecture, Computer Engineering, Computer Sciences, Databases and Information Systems, Engineering, Environmental Education, Environmental Engineering, Geomorphology, Hydrology, Systems and Communications, Water Resource Management

The objective of this paper is to present the initial illustration of a cyberinfrastructure named the River MORPhology Information System (RIMORPHIS) that addresses the current limitations related to river morphology data and tools. A new specification for data and semantics on river morphology datasets has been developed to support the web-based platform for discovering and visualization of [...]

Unsupervised Structural Damage Assessment from Space using the Segment Anything Model (USDA-SAM): A Case Study of the 2023 Türkiye Earthquake

Sudharshan Balaji, Oktay Karakus

Published: 2024-01-23
Subjects: Computer Sciences, Earth Sciences, Engineering, Environmental Sciences

This paper explores advanced deep learning methods, specifically utilising the Segment Anything Model (SAM) along with image processing techniques, to evaluate the structural damages caused by the devastating earthquake that occurred in Turkey on February 6, 2023. Leveraging exceptionally high-resolution pre- and post-disaster imagery provided by Maxar Technologies, this paper showcases the [...]

Deep learning with simulated laser scanning data for 3D point cloud classification

Alberto M. Esmorís, Hannah Weiser, Lukas Winiwarter, et al.

Published: 2024-01-16
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Numerical Analysis and Scientific Computing, Other Earth Sciences

Laser scanning is an active remote sensing technique applied in many disciplines to acquire state-of-the-art spatial measurements. Semantic labeling is often necessary to extract information from the raw point cloud. Deep learning methods constitute a data-hungry solution for the semantic segmentation of point clouds. In this work, we investigate the use of simulated laser scanning for training [...]

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