GeoAI at ACM SIGSPATIAL: progress, challenges, and future directions

Geospatial artificial intelligence (GeoAI) is an interdisciplinary field that has received tremendous attention from both academia and industry in recent years. This article reviews the series of GeoAI workshops held at the Association for Computing Machinery (ACM) International Conference on Advances in Geographic Information Systems (SIGSPATIAL) since 2017. These workshops have provided researchers a forum to present GeoAI advances covering a wide range of topics, such as geospatial image processing, transportation modeling, public health, and digital humanities. We provide a summary of these topics and the research articles presented at the 2017, 2018, and 2019 GeoAI workshops. We conclude with a list of open research directions for this rapidly advancing field.

[1]  Ying Zhang,et al.  Multi-scale Graph Convolutional Network for Intersection Detection from GPS Trajectories , 2019, GeoAI@SIGSPATIAL.

[2]  Abeed Sarker,et al.  Deep neural networks and distant supervision for geographic location mention extraction , 2018, Bioinform..

[3]  Vaibhav Kulkarni,et al.  Generating synthetic mobility traffic using RNNs , 2017, GeoAI@SIGSPATIAL.

[4]  Pengfei Xu,et al.  A Traffic Sign Discovery Driven System for Traffic Rule Updating , 2019, GeoAI@SIGSPATIAL.

[5]  Gady Agam,et al.  ChangeNet: Learning to Detect Changes in Satellite Images , 2019, GeoAI@SIGSPATIAL.

[6]  Gang Wang,et al.  Deep Learning-Based Classification of Hyperspectral Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[7]  Shenghua Gao,et al.  Encoding Crowd Interaction with Deep Neural Network for Pedestrian Trajectory Prediction , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[8]  Craig A. Knoblock,et al.  Automatic alignment of geographic features in contemporary vector data and historical maps , 2017, GeoAI@SIGSPATIAL.

[9]  Daniel Jurafsky,et al.  Word embeddings quantify 100 years of gender and ethnic stereotypes , 2017, Proceedings of the National Academy of Sciences.

[10]  Yao-Yi Chiang,et al.  Kartta Labs: Unrendering Historical Maps , 2019, GeoAI@SIGSPATIAL.

[11]  Aiman Soliman,et al.  Keras Spatial: Extending deep learning frameworks for preprocessing and on-the-fly augmentation of geospatial data , 2019, GeoAI@SIGSPATIAL.

[12]  Xiaobin Wang,et al.  DM_NLP at SemEval-2018 Task 12: A Pipeline System for Toponym Resolution , 2019, *SEMEVAL.

[13]  Shawn Newsam,et al.  Estimating the Spatial Resolution of Very High-Resolution Overhead Imagery , 2019, GeoAI@SIGSPATIAL.

[14]  Wenwen Li,et al.  Automated terrain feature identification from remote sensing imagery: a deep learning approach , 2018, Int. J. Geogr. Inf. Sci..

[15]  Yuanyuan Pao,et al.  Image-based classification of GPS noise level using convolutional neural networks for accurate distance estimation , 2017, GeoAI@SIGSPATIAL.

[16]  Krzysztof Janowicz,et al.  GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond , 2019, Int. J. Geogr. Inf. Sci..

[17]  Jean-Claude Thill,et al.  Enhancing Trip Distribution Prediction with Twitter Data: Comparison of Neural Network and Gravity Models , 2018, GeoAI@SIGSPATIAL.

[18]  Yanan Xin,et al.  Mapping Miscanthus Using Multi-Temporal Convolutional Neural Network and Google Earth Engine , 2019, GeoAI@SIGSPATIAL.

[19]  Andrew Crooks,et al.  Assessing the placeness of locations through user-contributed content , 2019, GeoAI@SIGSPATIAL.

[20]  Jiue-An Yang,et al.  Contextualizing Space and Time for GeoAI JITAIs (Just-in-Time Adaptive Interventions) , 2019, GeoAI@SIGSPATIAL.

[21]  Tao Sun,et al.  Combining Satellite Imagery and GPS Data for Road Extraction , 2018, GeoAI@SIGSPATIAL.

[22]  Orhun Aydin,et al.  SKATER-CON: Unsupervised Regionalization via Stochastic Tree Partitioning within a Consensus Framework Using Random Spanning Trees: Research Paper , 2018, GeoAI@SIGSPATIAL.

[23]  Bin Zhou,et al.  Recognizing terrain features on terrestrial surface using a deep learning model: an example with crater detection , 2017, GeoAI@SIGSPATIAL.

[24]  Martin Jägersand,et al.  Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[25]  Jun Li,et al.  Vision-based UAVs Aerial Image Localization: A Survey , 2018, GeoAI@SIGSPATIAL.

[26]  Patricia Murrieta-Flores,et al.  Toponym matching through deep neural networks , 2018, Int. J. Geogr. Inf. Sci..

[27]  Li Linlin,et al.  DM_NLP at SemEval-2018 Task 12: A Pipeline System for Toponym Resolution. , 2019, NAACL 2019.

[28]  Robert Stewart,et al.  Apache Spark Accelerated Deep Learning Inference for Large Scale Satellite Image Analytics , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[29]  Zonglin Meng,et al.  Urban Flood Mapping with Residual Patch Similarity Learning , 2019, GeoAI@SIGSPATIAL.

[30]  Shawn D. Newsam,et al.  GeoAI 2018 workshop report the 2nd ACM SIGSPATIAL international workshop on GeoAI: AI for geographic knowledge discovery seattle, WA, USA - November 6, 2018 , 2019, SIGSPACIAL.

[31]  Jiangye Yuan,et al.  Learning Building Extraction in Aerial Scenes with Convolutional Networks , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Shaowen Wang.  A CyberGIS Framework for the Synthesis of Cyberinfrastructure, GIS, and Spatial Analysis , 2010 .

[33]  Sujing Wang,et al.  Aconcagua: A Novel Spatiotemporal Emotion Change Analysis Framework , 2018, GeoAI@SIGSPATIAL.

[34]  Chaiyaphum Siripanpornchana,et al.  A machine learning approach to estimate median income levels of sub-districts in Thailand using satellite and geospatial data , 2019, GeoAI@SIGSPATIAL.

[35]  Yao Shen,et al.  An application of convolutional neural network in street image classification: the case study of london , 2017, GeoAI@SIGSPATIAL.

[36]  Begüm Demir,et al.  Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding , 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.

[37]  Zhou Xu,et al.  Vehicle Point of Interest Detection Using In-Car Data , 2018, GeoAI@SIGSPATIAL.

[38]  Xiao Xiang Zhu,et al.  Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources , 2017, IEEE Geoscience and Remote Sensing Magazine.

[39]  Bo Du,et al.  Scene Classification via a Gradient Boosting Random Convolutional Network Framework , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[40]  Ye Li,et al.  A Deep Residual Network Integrating Spatial-temporal Properties to Predict Influenza Trends at an Intra-urban Scale , 2018, GeoAI@SIGSPATIAL.

[41]  Jiangye Yuan,et al.  Building Extraction at Scale Using Convolutional Neural Network: Mapping of the United States , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[42]  T. Edwin Chow,et al.  When GeoAI Meets the Crowd , 2018, GeoAI@SIGSPATIAL.

[43]  Shivangi Srivastava,et al.  Multilabel Building Functions Classification from Ground Pictures using Convolutional Neural Networks , 2018, GeoAI@SIGSPATIAL.

[44]  David S. Ebert,et al.  City-level Geolocation of Tweets for Real-time Visual Analytics , 2019, GeoAI@SIGSPATIAL.

[45]  Qingquan Li,et al.  Visual landmark sequence-based indoor localization , 2017, GeoAI@SIGSPATIAL.

[46]  Wei Tu,et al.  STIETR: Spatial-temporal Intelligent E-Taxi Recommendation System Using GPS Trajectories , 2019, GeoAI@SIGSPATIAL.

[47]  Devis Tuia,et al.  Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data , 2018, Int. J. Geogr. Inf. Sci..

[48]  Michele Volpi,et al.  Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[49]  Benjamin Swan,et al.  How Good is Good Enough?: Quantifying the Effects of Training Set Quality , 2018, GeoAI@SIGSPATIAL.

[50]  Grant McKenzie,et al.  GeoAI 2017 workshop report: the 1st ACM SIGSPATIAL International Workshop on GeoAI: @AI and Deep Learning for Geographic Knowledge Discovery: Redondo Beach, CA, USA - November 7, 2016 , 2018, SIGSPACIAL.

[51]  Yanhua Li,et al.  Imitation Learning from Human-Generated Spatial-Temporal Data , 2019, GeoAI@SIGSPATIAL.

[52]  Haoyi Xiong,et al.  A Deep Learning based Illegal Parking Detection Platform , 2019, GeoAI@SIGSPATIAL.

[53]  Martin Tomko,et al.  Finding equivalent keys in openstreetmap: semantic similarity computation based on extensional definitions , 2017, GeoAI@SIGSPATIAL.

[54]  Krzysztof Janowicz,et al.  From ITDL to Place2Vec: Reasoning About Place Type Similarity and Relatedness by Learning Embeddings From Augmented Spatial Contexts , 2017, SIGSPATIAL/GIS.

[55]  Sara J. Graves,et al.  Deep learning for multisensor image resolution enhancement , 2017, GeoAI@SIGSPATIAL.

[56]  Stephen Law,et al.  Learning from Discovering: An unsupervised approach to Geographical Knowledge Discovery using street level and street network images , 2019, ArXiv.

[57]  John Krumm,et al.  Land Use Inference from Mobility Traces , 2019, GeoAI@SIGSPATIAL.