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Found 20 papers, 2 papers with code
GeoAI Reproducibility and Replicability: a computational and spatial perspective
We then discuss the factors that may cause the lack of R&R in GeoAI research, with an emphasis on (1) the selection and use of training data; (2) the uncertainty that resides in the GeoAI model design, training, deployment, and inference processes; and more importantly (3) the inherent spatial heterogeneity of geospatial data and processes.
A2CI: A Cloud-based, Service-oriented Geospatial Cyberinfrastructure to Support Atmospheric Research
Big earth science data offers the scientific community great opportunities.
Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model's Generalizability in Permafrost Mapping
To evaluate the performance of large AI vision models, especially Meta's Segment Anything Model (SAM), we implemented different instance segmentation pipelines that minimize the changes to SAM to leverage its power as a foundation model.
GeoAI in Social Science
GeoAI, or geospatial artificial intelligence, is an exciting new area that leverages artificial intelligence (AI), geospatial big data, and massive computing power to solve problems with high automation and intelligence.
Development of a Deep Learning Method to Identify Acute Ischemic Stroke Lesions on Brain CT
DL methods can be designed for AIS lesion detection on CT using the vast quantities of routinely-collected CT brain scan data.
Challenges of building medical image datasets for development of deep learning software in stroke
Despite the large amount of brain CT data generated in clinical practice, the availability of CT datasets for deep learning (DL) research is currently limited.
Assessment of a new GeoAI foundation model for flood inundation mapping
Vision foundation models are a new frontier in Geospatial Artificial Intelligence (GeoAI), an interdisciplinary research area that applies and extends AI for geospatial problem solving and geographic knowledge discovery, because of their potential to enable powerful image analysis by learning and extracting important image features from vast amounts of geospatial data.
Real-time GeoAI for High-resolution Mapping and Segmentation of Arctic Permafrost Features
This paper introduces a real-time GeoAI workflow for large-scale image analysis and the segmentation of Arctic permafrost features at a fine-granularity.
Semantic Similarity Measure of Natural Language Text through Machine Learning and a Keyword-Aware Cross-Encoder-Ranking Summarizer -- A Case Study Using UCGIS GIS&T Body of Knowledge
Initiated by the University Consortium of Geographic Information Science (UCGIS), GIS&T Body of Knowledge (BoK) is a community-driven endeavor to define, develop, and document geospatial topics related to geographic information science and technologies (GIS&T).
ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging
We investigate the best way to generate the saliency maps employed in our architecture and propose a way to obtain them from adversarially generated counterfactual images.
Explainable GeoAI: Can saliency maps help interpret artificial intelligence's learning process? An empirical study on natural feature detection
This paper compares popular saliency map generation techniques and their strengths and weaknesses in interpreting GeoAI and deep learning models' reasoning behaviors, particularly when applied to geospatial analysis and image processing tasks.
GeoAI for Knowledge Graph Construction: Identifying Causality Between Cascading Events to Support Environmental Resilience Research
Knowledge graph technology is considered a powerful and semantically enabled solution to link entities, allowing users to derive new knowledge by reasoning data according to various types of reasoning rules.
4G 5G Cell-level Multi-indicator Forecasting based on Dense-MLP
With the development of 4G/5G, the rapid growth of traffic has caused a large number of cell indicators to exceed the warning threshold, and network quality has deteriorated.
Geo-Situation for Modeling Causality of Geo-Events in Knowledge Graphs
This paper proposes a framework for representing and reasoning causality between geographic events by introducing the notion of Geo-Situation.
Ensemble Learning For Mega Man Level Generation
Procedural content generation via machine learning (PCGML) is the process of procedurally generating game content using models trained on existing game content.
Learning from Counting: Leveraging Temporal Classification for Weakly Supervised Object Localization and Detection
This paper reports a new solution of leveraging temporal classification to support weakly supervised object detection (WSOD).
Artificial Intelligence Approaches
Artificial Intelligence (AI) has received tremendous attention from academia, industry, and the general public in recent years.
Domain Independent SVM for Transfer Learning in Brain Decoding
We use public data to construct 13 transfer learning tasks in brain decoding, including three interesting multi-source transfer tasks.
Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI
While functional magnetic resonance imaging (fMRI) is important for healthcare/neuroscience applications, it is challenging to classify or interpret due to its multi-dimensional structure, high dimensionality, and small number of samples available.
Deep Convolutional Neural Networks for Map-Type Classification
To facilitate establishing an automatic approach for accessing the needed map, this paper reports our investigation into using deep learning techniques to recognize seven types of map, including topographic map, terrain map, physical map, urban scene map, the National Map, 3D map, nighttime map, orthophoto map, and land cover classification map.
