H029
Artificial Intelligence and Remote Sensing: Enabling The Next Generation of Global Applications for Sustainable Development
Session ID#: 76602
Session Description:
Over the last decade, an astonishing number of studies have demonstrated the potential of Artificial Intelligence (AI) in detecting, modeling, and forecasting complex environmental and socio-economic processes from Earth Observations (EO). The path ahead seems even brighter; novel AI techniques are being developed every year, private and public organizations are launching new EO satellites on a regular basis, and specialized, highly-scalable computational platforms are allowing users to build applications that were inaccessible before. As we partake in this fast-paced golden age for EO and AI, we have to be mindful in realizing the potential of these innovative technologies where it matters the most: improving the wellbeing of humanity and planet Earth. This session solicits presentations 1) showcasing promising use-cases of AI and EO in sustainable development, 2) addressing the main technological challenges left to solve, and 3) exploring the next generation of applications in this domain.
Index Terms:
0480 Remote sensing [BIOGEOSCIENCES]
1640 Remote sensing [GLOBAL CHANGE]
1855 Remote sensing [HYDROLOGY]
1942 Machine learning [INFORMATICS]
Primary Convener: Hamed Alemohammad, Radiant Earth Foundation, San Francisco, CA, United States
Convener: George Azzari, Atlas AI, Palo Alto, CA, United States
Primary Liaison: Hamed Alemohammad, Radiant Earth Foundation, San Francisco, CA, United States
Rage against the machine: ecological learning from machine learning (497181)
Qiuyan Yu1, Wenjie Ji2, Christopher Wade Ross1, Julius Anchang3, Lara Prihodko4 and Niall P Hanan3, (1)New Mexico State University Main Campus, Plant and Environmental Sciences, Las Cruces, NM, United States, (2)New Mexico State University, Plant and Environment Sciences, Las Cruces, NM, United States, (3)New Mexico State University Main Campus, Plant and Environment Sciences, Las Cruces, NM, United States, (4)New Mexico State University Main Campus, Animal and range Sciences, Las Cruces, NM, United States
An Open Machine Learning Challenge to Map Urban Development and Resilience in Diverse African Cities from Aerial Imagery (499042)
David Luo1, Vivien Deparday1, Cristiano Giovando1, Caroline Gevaert1, Robert Emanuele2, Simon Kassel2, Esther Needham2, Greg Lipstein3, Peter Bull3, Emily Miller3 and Isaac Slavitt3, (1)Global Facility for Disaster Reduction and Recovery, World Bank, Washington, DC, United States, (2)Azavea, Philadelphia, PA, United States, (3)DrivenData, Denver, CO, United States
Improving maps of smallholder-dominated croplands through tight integration of human and machine intelligence (546886)
Lyndon D Estes1, Su Ye1, Lei Song2, Ryan Barry Avery3, Dennis McRitchie4, Ronald Eastman5, Stephanie R Debats6 and Kelly K Caylor7,8, (1)Clark University, Graduate School of Geography, Worcester, MA, United States, (2)Clark University, Graduate School of Geography, Worcester, United States, (3)University of California Santa Barbara, Santa Barbara, CA, United States, (4)Self Employed, Tucson, AZ, United States, (5)Clark Labs, Worcester, MA, United States, (6)Uber ATG, San Francisco, United States, (7)University of California Santa Barbara, Earth Research Institute, Santa Barbara, CA, United States, (8)University of California Santa Barbara, Bren School of Environmental Science and Management, Santa Barbara, CA, United States
Assessing Human Security with Remote Sensing and GeoAI (Invited) (572172)
Budhendra L Bhaduri1, Dalton Lunga2, Jacob Arndt1, Eric Weber1, Amy Rose1 and Robert Stewart1, (1)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (2)Oak Ridge National Laboratory, Geographic Data Science, Oak Ridge, TN, United States
Towards Automation of Statewide Impervious Surface Mapping Using High Resolution Aerial Imagery and Deep Convolutional Neural Networks (574125)
Jianfeng Huang, Sun Yat-sen University, School of Geography and Planning, Guangzhou, China, Qinchuan Xin, Sun Yat-Sen University, Ministry of Education Key Laboratory for Earth System Modeling, Guangzhou, China and Ying Sun, Sun Yat-Sen University, School of Geography and Planning, Guangzhou, China
Using Artificial Intelligence to forecast harmful algal blooms in Lake Atitlan, Guatemala (584182)
Africa Ixmucane Flores Cordova1, John Robert Mecikalski2, Xiang Li3, Christopher Jewett3, Emil A. Cherrington1, Betzy E. Hernández2 and Robert Griffin2, (1)University of Alabama in Huntsville, Earth System Science Center, Huntsville, AL, United States, (2)University of Alabama in Huntsville, Huntsville, AL, United States, (3)University of Alabama in Huntsville, Huntsville, United States
Current Trends in Satellite Land Remote Sensing (594024)
Jon Christopherson1, Kimberly Casey2,3 and Shankar N. Ramaseri Chandra1, (1)KBR, contractor to USGS EROS, Sioux Falls, SD, United States, (2)U.S. Geological Survey, Reston, VA, United States, (3)NASA Goddard Space Flight Center, Greenbelt, MD, United States
Development of Water Masks using Synthetic Aperture Radar via a Fully Convolutional Neural Network at the Alaska Satellite Facility (596392)
Thomas Andrew Logan1, Kirk Hogenson1, Rohan Wheeden2, D McKade Sorenson2 and George T Meier2, (1)University of Alaska Fairbanks, Fairbanks, AK, United States, (2)University of Alaska Fairbanks, Fairbanks, United States
Mapping Crop Types in India with Crowdsourced Data and Deep Learning (600258)
Sherrie Wang1, Stefania Di Tommaso1, Joey Faulkner2, Thomas Friedel2, Alexander Kennepohl2, Robert Strey2 and David B Lobell1, (1)Stanford University, Stanford, CA, United States, (2)PEAT GmbH, Berlin, Germany
Exploration of artificial intelligence approaches to Earth observing remote sensing (605918)
Ahmed Eleish1, Luke A Winslow2, Anirudh Prabhu1, Michael Kelly3, Harry Kolar4, Kevin C Rose5 and Peter Arthur Fox1, (1)Rensselaer Polytechnic Institute, Tetherless World Constellation, Troy, NY, United States, (2)USGS, Office of Water Information, Middleton, WI, United States, (3)IBM Research USA, Yorktown Heights, United States, (4)IBM Research USA, Yorktown Heights, NY, United States, (5)Rensselaer Polytechnic Institute, Department of Biological Sciences, Troy, NY, United States
Predicting Future Deforestation in the Congo Basin Using Machine Learning (612166)
Kristine Lister, Would Resources Institute, Washington, NY, United States, Brookie P Guzder-Williams, World Resources Institute, San Francisco, CA, United States and Liz Goldman, World Resources Institute, Washington, United States
Developing a Machine-Learning-Based Processing Infrastructure for Twitter and Other Crowdsourced Data (612223)
William L Teng1, Arif Albayrak1, Laura Yu Zheng2, Matteo Russo3, Rachel Li4 and Long Pham5, (1)ADNET Systems Inc. Greenbelt, Greenbelt, MD, United States, (2)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (3)Princeton University, Princeton, United States, (4)University of California Berkeley, Berkeley, CA, United States, (5)NASA - GSFC, Greenbelt, MD, United States
Mapping Urban India: Comprehensive Land Use/Land Cover Classification of Urban Areas Using Public Imagery and Machine Learning (613597)
Peter Kerins, World Resources Institute, Washington, DC, United States, Brookie P Guzder-Williams, World Resources Institute, San Francisco, CA, United States, Taufiq Rashid, World Resources Institute, Brentwood, MD, United States, Eric Mackres, World Resources Insititute, Washington, United States and Eric Pietraszkiewicz, World Resources Institute, Washington, United States
