skip to main content
research-article

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

Published:17 December 2019Publication History
Skip Abstract Section

Abstract

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.

References

  1. O. Aydin, M. V. Janikas, R. Assunção, and T.-H. Lee. SKATER-CON: Unsupervised regionalization via stochastic tree partitioning within a consensus framework using random spanning trees. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 33--42. ACM, 2018.Google ScholarGoogle Scholar
  2. Y. Chen, Z. Lin, X. Zhao, G. Wang, and Y. Gu. Deep learning-based classification of hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6):2094--2107, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  3. Y. Chen, X. Ouyang, and G. Agam. ChangeNet: Learning to detect changes in satellite images. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 24--31. ACM, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. T. E. Chow. When GeoAI meets the crowd. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI'18, pages 52--53, New York, NY, USA, 2018. ACM.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. C. B. Collins, J. M. Beck, S. M. Bridges, J. A. Rushing, and S. J. Graves. Deep learning for multisensor image resolution enhancement. In Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery, pages 37--44. ACM, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248--255. Ieee, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  7. U. J. Dorji, A. Plangprasopchok, N. Surasvadi, and C. Siripanpornchana. A machine learning approach to estimate median income levels of sub-districts in Thailand using satellite and geospatial data. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 11--14. ACM, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. W. Duan, Y.-Y. Chiang, C. A. Knoblock, V. Jain, D. Feldman, J. H. Uhl, and S. Leyk. Automatic alignment of geographic features in contemporary vector data and historical maps. In Proceedings of the 1st workshop on artificial intelligence and deep learning for geographic knowledge discovery, pages 45--54. ACM, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. K. Elgarroussi, S. Wang, R. Banerjee, and C. F. Eick. Aconcagua: A novel spatiotemporal emotion change analysis framework. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 54--61. ACM, 2018.Google ScholarGoogle Scholar
  10. N. Garg, L. Schiebinger, D. Jurafsky, and J. Zou. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences, 115(16):E3635-E3644, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  11. Y. Hu, S. Gao, S. Newsam, and D. Lunga. GeoAI 2018 workshop report the 2nd acm sigspatial international workshop on GeoAI: AI for geographic knowledge discovery seattle, wa, usa-november 6, 2018. SIGSPATIAL special, 10(3):16--16, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. K. Janowicz, S. Gao, G. McKenzie, Y. Hu, and B. Bhaduri. GeoAI: Spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. International Journal of Geographical Information Science, pages 1--13, 2020.Google ScholarGoogle ScholarCross RefCross Ref
  13. J. Krumm and K. Krumm. Land use inference from mobility traces. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 1--4. ACM, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. V. Kulkarni and B. Garbinato. Generating synthetic mobility traffic using rnns. In Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery, pages 1--4. ACM, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. S. Law and D. M. N. Alvarez. An unsupervised approach to geographical knowledge discovery using street level and street network images. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 56--65. ACM, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. S. Law, Y. Shen, and C. Seresinhe. An application of convolutional neural network in street image classification: The case study of london. In Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery, pages 5--9. ACM, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Q. Li, J. Zhu, T. Liu, J. Garibaldi, Q. Li, and G. Qiu. Visual landmark sequence-based indoor localization. In Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery, pages 14--23. ACM, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. W. Li and C.-Y. Hsu. Automated terrain feature identification from remote sensing imagery: a deep learning approach. International Journal of Geographical Information Science, pages 1--24, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  19. W. Li, B. Zhou, C.-Y. Hsu, Y. Li, and F. Ren. Recognizing terrain features on terrestrial surface using a deep learning model: An example with crater detection. In Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery, pages 33--36. ACM, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Y. Li and W. Huang. Imitation learning from human-generated spatial-temporal data. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 9--10. ACM, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. H. Liang and S. Newsam. Estimating the spatial resolution of very high-resolution overhead imagery. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 77--80. ACM, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. D. Lunga, J. Gerrand, H. L. Yang, C. Layton, and R. Stewart. Apache spark accelerated deep learning inference for large scale satellite image analytics, 2019.Google ScholarGoogle Scholar
  23. A. Magge, D. Weissenbacher, A. Sarker, M. Scotch, and G. Gonzalez-Hernandez. Deep neural networks and distant supervision for geographic location mention extraction. Bioinformatics, 34(13):i565--i573, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  24. K. Mai, W. Tu, Q. Li, H. Ye, T. Zhao, and Y. Zhang. STIETR: Spatial-temporal intelligent e-taxi recommendation system using gps trajectories. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 5--8. ACM, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. I. Majic, S. Winter, and M. Tomko. Finding equivalent keys in openstreetmap: semantic similarity computation based on extensional definitions. In Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery, pages 24--32. ACM, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. H. Mao, Y. Hu, B. Kar, S. Gao, and G. McKenzie. 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. SIGSPATIAL Special, 9(3):25--25, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. D. Marcos, M. Volpi, B. Kellenberger, and D. Tuia. Land cover mapping at very high resolution with rotation equivariant cnns: Towards small yet accurate models. ISPRS journal of photogrammetry and remote sensing, 145:96--107, 2018.Google ScholarGoogle Scholar
  28. J. Murphy, Y. Pao, and A. Haque. Image-based classification of gps noise level using convolutional neural networks for accurate distance estimation. In Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery, pages 10--13. ACM, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. B. Peng, X. Liu, Z. Meng, and Q. Huang. Urban flood mapping with residual patch similarity learning. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 40--47. ACM, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. N. Pourebrahim, S. Sultana, J.-C. Thill, and S. Mohanty. Enhancing trip distribution prediction with twitter data: comparison of neural network and gravity models. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 5--8. ACM, 2018.Google ScholarGoogle Scholar
  31. R. Santos, P. Murrieta-Flores, P. Calado, and B. Martins. Toponym matching through deep neural networks. International Journal of Geographical Information Science, 32(2):324--348, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  32. M. Siam, S. Elkerdawy, M. Jagersand, and S. Yogamani. Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pages 1--8. IEEE, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. L. Snyder, M. Karimzadeh, R. Chen, and D. Ebert. City-level geolocation of tweets for real-time visual analytics. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 85--88. ACM, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. A. Soliman and J. Terstriep. Keras Spatial: Extending deep learning frameworks for preprocessing and on-the-fly augmentation of geospatial data. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 69--76. ACM, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. S. Srivastava, J. E. Vargas Muñoz, S. Lobry, and D. Tuia. Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data. International Journal of Geographical Information Science, pages 1--20, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  36. S. Srivastava, J. E. Vargas-Muñoz, D. Swinkels, and D. Tuia. Multilabel building functions classification from ground pictures using convolutional neural networks. In Proceedings of the 2nd ACM SIGSPATIAL international workshop on AI for geographic knowledge discovery, pages 43--46. ACM, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. G. Sumbul, M. Charfuelan, B. Demir, and V. Markl. Bigearthnet: A large-scale benchmark archive for remote sensing image understanding. CoRR, abs/1902.06148, 2019.Google ScholarGoogle Scholar
  38. T. Sun, Z. Di, and Y. Wang. Combining satellite imagery and gps data for road extraction. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 29--32. ACM, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. B. Swan, M. Laverdiere, and H. L. Yang. How good is good enough?: Quantifying the effects of training set quality. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 47--51. ACM, 2018.Google ScholarGoogle Scholar
  40. S. Tavakkol, Y.-Y. Chiang, T. Waters, F. Han, K. Prasad, and R. Kiveris. Kartta labs: Unrendering historical maps. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 48--51. ACM, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. J. Van Hinsbergh, N. Griffiths, P. Taylor, A. Thomason, Z. Xu, and A. Mouzakitis. Vehicle point of interest detection using in-car data. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 1--4. ACM, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. S. Wang. A cybergis framework for the synthesis of cyberinfrastructure, gis, and spatial analysis. Annals of the Association of American Geographers, 100(3):535--557, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  43. X. Wang, C. Ma, H. Zheng, C. Liu, P. Xie, L. Li, and L. Si. Dm_nlp at semeval-2018 task 12: A pipeline system for toponym resolution. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 917--923, Stroudsburg, PA, USA, 2019. ACL.Google ScholarGoogle ScholarCross RefCross Ref
  44. G. Xi, L. Yin, Y. Li, and S. Mei. A deep residual network integrating spatial-temporal properties to predict influenza trends at an intra-urban scale. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 19--28. ACM, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Y. Xin and P. R. Adler. Mapping miscanthus using multi-temporal convolutional neural network and google earth engine. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 81--84. ACM, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. T. Xing, Y. Gu, Z. Song, Z. Wang, Y. Meng, N. Ma, P. Xu, R. Hu, and H. Chai. A traffic sign discovery driven system for traffic rule updating. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 52--55. ACM, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Y. Xu, L. Pan, C. Du, J. Li, N. Jing, and J. Wu. Vision-based uavs aerial image localization: A survey. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 9--18. ACM, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Y. Xu, Z. Piao, and S. Gao. Encoding crowd interaction with deep neural network for pedestrian trajectory prediction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5275--5284, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  49. B. Yan, K. Janowicz, G. Mai, and S. Gao. From itdl to place2vec: Reasoning about place type similarity and relatedness by learning embeddings from augmented spatial contexts. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, page 35. ACM, 2017.Google ScholarGoogle Scholar
  50. H. L. Yang, J. Yuan, D. Lunga, M. Laverdiere, A. Rose, and B. Bhaduri. Building extraction at scale using convolutional neural network: Mapping of the united states. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(8):2600--2614, Aug 2018.Google ScholarGoogle ScholarCross RefCross Ref
  51. J.-A. Yang and M. Jankowska. Contextualizing space and time for geoai jitais (just-in-time adaptive interventions). In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 66--68. ACM, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Y. Yin, A. Sunderrajan, X. Huang, J. Varadarajan, G. Wang, D. Sahrawat, Y. Zhang, R. Zimmermann, and S.-K. Ng. Multi-scale graph convolutional network for intersection detection from gps trajectories. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 36--39. ACM, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Z. Yin, H. Xiong, X. Zhou, D. Goldberg, D. Bennett, and C. Zhang. A deep learning based illegal parking detection platform. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 32--35. ACM, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. J. Yuan. Learning building extraction in aerial scenes with convolutional networks. IEEE transactions on pattern analysis and machine intelligence, 40(11):2793--2798, 2017.Google ScholarGoogle Scholar
  55. X. Yuan and A. Crooks. Assessing the placeness of locations through user-contributed content. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 15--23. ACM, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. F. Zhang, B. Du, and L. Zhang. Scene classification via a gradient boosting random convolutional network framework. IEEE Transactions on Geoscience and Remote Sensing, 54(3):1793--1802, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  57. X. X. Zhu, D. Tuia, L. Mou, G.-S. Xia, L. Zhang, F. Xu, and F. Fraundorfer. Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4):8--36, 2017.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. GeoAI at ACM SIGSPATIAL: progress, challenges, and future directions
    Index terms have been assigned to the content through auto-classification.

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image SIGSPATIAL Special
      SIGSPATIAL Special  Volume 11, Issue 2
      July 2019
      39 pages
      EISSN:1946-7729
      DOI:10.1145/3377000
      Issue’s Table of Contents

      Copyright © 2019 Copyright is held by the owner/author(s)

      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 17 December 2019

      Check for updates

      Qualifiers

      • research-article

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader