A comprehensive GeoAI review: Progress, Challenges and Outlooks
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Abstract (arXiv)
In recent years, Geospatial Artificial Intelligence (GeoAI) has gained traction in the most relevant research works and industrial applications, while also becoming involved in various fields of use. This paper offers a comprehensive review of GeoAI as a synergistic concept applying Artificial Intelligence (AI) methods and models to geospatial data. A preliminary study is carried out, identifying the methodology of the work, the research motivations, the issues and the directions to be tracked, followed by exploring how GeoAI can be used in various interesting fields of application, such as precision agriculture, environmental monitoring, disaster management and urban planning. Next, a statistical and semantic analysis is carried out, followed by a clear and precise presentation of the challenges facing GeoAI. Then, a concrete exploration of the future prospects is provided, based on several informations gathered during the census. To sum up, this paper provides a complete overview of the correlation between AI and the geospatial domain, while mentioning the researches conducted in this context, and emphasizing the close relationship linking GeoAI with other advanced concepts such as geographic information systems (GIS) and large-scale geospatial data, known as big geodata. This will enable researchers and scientific community to assess the state of progress in this promising field, and will help other interested parties to gain a better understanding of the issues involved.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 59 of its 384 references that are in the map. First posted on arXiv 16 Dec 2024.
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59 of 384 references.
References · 59
- 2017Attention Is All You Needcited by 59,932
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2017Mask R-CNNcited by 7,259
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
38 more
- 2016Feature Pyramid Networks for Object Detectioncited by 5,021
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
- 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
- 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
- 2019Advances and Open Problems in Federated Learningcited by 3,021
- 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
- 2016Pyramid Scene Parsing Networkcited by 2,472
- 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
- 2019Searching for MobileNetV3cited by 1,984
- 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
- 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,509
- 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
- 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
- 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,256
- 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
- 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,128
- 2017In-Datacenter Performance Analysis of a Tensor Processing Unitcited by 610
- 2016Generating Videos with Scene Dynamicscited by 376
- 2016AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classificationcited by 343
- 2017LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentationcited by 212
- 2017Deep Learning for Precipitation Nowcasting: A Benchmark and A New Modelcited by 180
- 2018xView: Objects in Context in Overhead Imagerycited by 145
- 2019xBD: A Dataset for Assessing Building Damage from Satellite Imagerycited by 138
- 2018Recycle-GAN: Unsupervised Video Retargetingcited by 89
- 2020Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadwayscited by 77
- 2018Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modelingcited by 39
- 2020Aerial Imagery Pile burn detection using Deep Learning: the FLAME datasetcited by 34
- 2021Autoencoders for unsupervised anomaly detection in high energy physicscited by 32
- 2024Remote Sensing ChatGPT: Solving Remote Sensing Tasks with ChatGPT and Visual Modelscited by 30
- 2022xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagerycited by 25
- 2019DEVDAN: Deep Evolving Denoising Autoencodercited by 16
- 2016Learning Shared Representations in Multi-task Reinforcement Learningcited by 15
- 2023Explainable GeoAI: Can saliency maps help interpret artificial intelligence's learning process? An empirical study on natural feature detectioncited by 14
- 2024AGBD: A Global-scale Biomass Datasetcited by 13
- 2016Optimizing Performance of Recurrent Neural Networks on GPUscited by 12
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