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The convergence of artificial intelligence (AI) and Earth observation (EO) technologies has brought geoscience and remote sensing into an era of unparalleled capabilities.
“Bias in computer systems”
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“Toward a comprehensive view of uncertainty in remote sensing analysis”
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“Tobler’s First Law and Spatial Analysis”
Harvey. Miller · 2004
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“Why is there an early spring cooling shift downstream of the Tibetan Plateau?”
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“Intraseasonal teleconnection between the summer Eurasian wave train and the Indian monsoon”
Q. Ding and B. Wang · 2007
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“Geo-Wiki: An online platform for improving global land cover”
Steffen Fritz et al · 2011
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“Climate change: new dimensions in disaster risk, exposure, vulnerability, and resilience”
Allan Lavell et al · 2012
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“Machine learning that matters”
Kiri Wagstaff · 2012
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“Seven types of privacy”
Rachel Finn, David Wright and Michael Friedewald · 2013
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“Irrigation in California’s Central Valley strengthens the southwestern U.S. water cycle”
M.-H. Lo and J. Famiglietti · 2013
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“Explaining and harnessing adversarial examples”
Ian Goodfellow, Jonathon Shlens and Christian Szegedy · 2014
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“Uncertainty quantification of GEOS-5 L-band radiative transfer model parameters using Bayesian inference and SMOS observations”
Gabri“”elle De, Rolf Reichle and Jasper Vrugt · 2014
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“Good practices for estimating area and assessing accuracy of land change”
Pontus Olofsson et al · 2014
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“Replication of scientific research: addressing geoprivacy, confidentiality, and data sharing challenges in geospatial research”
Douglas. Richardson, Mei-Po Kwan, George Alter and Jean. McKendry · 2015
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“Land system science and sustainable development of the earth system: A global land project perspective”
Peter Verburg et al · 2015
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“Development of a global hybrid forest mask through the synergy of remote sensing, crowdsourcing and FAO statistics”
Dmitry Schepaschenko et al · 2015
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“Virtual constellations for global terrestrial monitoring”
Michael. Wulder et al · 2015
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“Transforming our world: the 2030 Agenda for Sustainable Development”, 2015
UN · 2015
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“The East Atlantic/West Russia (EA/WR) teleconnection in the North Atlantic: climate impact and relation to Rossby wave propagation”
Y.-K. Lim · 2015
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“Slums from space—15 years of slum mapping using remote sensing”
Monika Kuffer, Karin Pfeffer and Richard Sliuzas · 2016
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“Domain adaptation for the classification of remote sensing data: An overview of recent advances”
Devis Tuia, Claudio Persello and Lorenzo Bruzzone · 2016
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“” Why should i trust you?” Explaining the predictions of any classifier”
Marco Ribeiro, Sameer Singh and Carlos Guestrin · 2016
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“The FAIR Guiding Principles for scientific data management and stewardship”
Mark. Wilkinson et al · 2016
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“Effects of Irrigation on Summer Precipitation over the United States”
L. Pei · 2016
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“Where Does the Irrigation Water Go? An Estimate of the Contribution of Irrigation to Precipitation Using MERRA”
J. Wei · 2016
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“Deep learning in remote sensing: A comprehensive review and list of resources”
Xiao Zhu et al · 2017
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“IEEE standard review—Ethically aligned design: A vision for prioritizing human wellbeing with artificial intelligence and autonomous systems”
Kyarash Shahriari and Mana Shahriari · 2017
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“Decision-based adversarial attacks: Reliable attacks against black-box machine learning models”
Wieland Brendel, Jonas Rauber and Matthias Bethge · 2017
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“Deep fully convolutional networks for the detection of informal settlements in VHR images”
Claudio Persello and Alfred Stein · 2017
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“Trade-offs between environment and livelihoods: Bridging the global land use and food security discussions”
Patrick Meyfroidt · 2017
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“Revisiting ENSO/Indian Ocean dipole phase relationships”
M.F. Stuecker · 2017
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“New frontiers in spectral-spatial hyperspectral image classification: The latest advances based on mathematical morphology, Markov random fields, segmentation, sparse representation, and deep learning”
Pedram Ghamisi et al · 2018
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“Gender shades: Intersectional accuracy disparities in commercial gender classification”
Joy Buolamwini and Timnit Gebru · 2018
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“ACM code of ethics and professional conduct”
DW Gotterbarn et al · 2018
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“Are we underestimating urban poverty?”
Paula Lucci, Tanvi Bhatkal and Amina Khan · 2018
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“A critical review of high and very high-resolution remote sensing approaches for detecting and mapping slums: Trends, challenges and emerging opportunities”
Ron Mahabir et al · 2018
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“Adversarial examples in remote sensing”
Wojciech Czaja et al · 2018
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“Model risk: illuminating the black box”
Rob Black et al · 2018
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“Evaluating the societal impact of using drones to support urban upgrading projects”
Caroline Gevaert, Richard Sliuzas, Claudio Persello and George Vosselman · 2018
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“The scope of earth-observation to improve the consistency of the SDG slum indicator”
Monika Kuffer et al · 2018
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“Kenya slum demolished to make way for road”
BBC · 2018
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“Definitions of co-production and co-creation”
Taco Brandsen and Marlies Honingh · 2018
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“Ethics guidelines for trustworthy AI”
AI Hleg · 2019
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“Responsible artificial intelligence: how to develop and use AI in a responsible way”
Virginia Dignum · 2019
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“A comparative study of fairness-enhancing interventions in machine learning”
Sorelle Friedler et al · 2019
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“Fairness and Machine Learning. fairmlbook. org”, 2019
Solon Barocas, Moritz Hardt and Arvind Narayanan · 2019
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“The global landscape of AI ethics guidelines”
Anna Jobin, Marcello Ienca and Effy Vayena · 2019
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“Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead”
Cynthia Rudin · 2019
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“FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond”
David Frantz · 2019
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“Evolution of IOD-ENSO relationship at multiple time scales”
Y.-F. Sang, V.P. Singh and K. Xu · 2019
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“Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for AI”
Jessica Fjeld et al · 2020
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“Bias in Machine Learning–What is it Good for?”
Thomas Hellstr“”om, Virginia Dignum and Suna Bensch · 2020
Cited alongside, same era.
“Assessing the threat of adversarial examples on deep neural networks for remote sensing scene classification: Attacks and defenses”
Yonghao Xu, Bo Du and Liangpei Zhang · 2020
Cited alongside, same era.
“Characterizing uncertainty in forest remote sensing studies”
Henrik Persson and G“”oran Sthl · 2020
Cited alongside, same era.
“A game theoretic analysis of additive adversarial attacks and defenses”
Ambar Pal and Ren“’e Vidal · 2020
Cited alongside, same era.
“Toward a Collective Agenda on AI for Earth Science Data Analysis”
D. Tuia et al · 2020
Cited alongside, same era.
“Explainable AI for earth observation: A review including societal and regulatory perspectives”
Caroline. Gevaert · 2022
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“Revisiting the Past: Replicability of a Historic Long-Term Vegetation Dynamics Assessment in the Era of Big Data Analytics” Num Pages: 27
David Frantz et al · 2022
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“Unlocking Large-Scale Crop Field Delineation in Smallholder Farming Systems with Transfer Learning and Weak Supervision” Num Pages: 32
Sherrie Wang, François Waldner and David. Lobell · 2022
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“Deep learning and earth observation to support the sustainable development goals: Current approaches, open challenges, and future opportunities”
Claudio Persello et al · 2022
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“IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change”
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Ricardo Vinuesa et al · 2020
Cited alongside, same era.
“The European Green Deal”
C. Fetting · 2020
Cited alongside, same era.
“Importance and vulnerability of the world?s water towers”
W.W. Immerzeel · 2020
Cited alongside, same era.
“Carbontracker: Tracking and predicting the carbon footprint of training deep learning models”
Lasse F Anthony, Benjamin Kanding and Raghavendra Selvan · 2020
Cited alongside, same era.
“An overview of national AI strategies and policies”
Laura Galindo, Karine Perset and Francesca Sheeka · 2021
Cited alongside, same era.
“The Potential of Machine Learning for a More Responsible Sourcing of Critical Raw Materials”
Pedram Ghamisi et al · 2021
Cited alongside, same era.
“Recommendation on the ethics of artificial intelligence”
C UNESCO · 2021
Cited alongside, same era.
V. MAsson-Delmotte · 2022
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“Tackling Climate Change with Machine Learning”
D. Rolnick · 2022
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“Mitigation measures following landslides in sensitive clays, examples from Norway”
J.-S. L?Heureux · 2022
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“Winter and spring atmospheric rivers in High Mountain Asia: climatology, dynamics, and variability”
D. Nash · 2022
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“Maxar Publishes 2022 ESG Report — Maxar — maxar.com” [Accessed 21-05-2024], https://www.maxar.com/press-releases/maxar-publishes-2022-esg-report , 2022
Maxar Technologies · 2022
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“On the opportunities and challenges of foundation models for geospatial artificial intelligence”
Gengchen Mai et al · 2023
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“SpectralGPT: Spectral foundation model”
Danfeng Hong et al · 2023
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“Auditing geospatial datasets for biases: using global building datasets for disaster risk management”
Caroline Gevaert, Thomas Buunk and Marc Van Homberg · 2023
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“Data-Centric Machine Learning for Geospatial Remote Sensing Data”
Ribana Roscher et al · 2023
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“Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models”
Weikang Yu, Yonghao Xu and Pedram Ghamisi · 2023
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“Adversarial driving: Attacking end-to-end autonomous driving”
Han Wu et al · 2023
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“Cross-domain landslide mapping from large-scale remote sensing images using prototype-guided domain-aware progressive representation learning”
Xiaokang Zhang, Weikang Yu, Man-On Pun and Wenzhong Shi · 2023
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“Extending CAM-based XAI methods for Remote Sensing Imagery Segmentation”
Abdul Gizzini, Mustafa Shukor and Ali Ghandour · 2023
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“Garbage in, garbage out: mitigating risks and maximizing benefits of AI in research”
Brooks Hanson et al · 2023
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“Conducting Ethically Mindful Earth Observation Research: The Case of Slum Mapping”
Mrinalini Kochupillai and Hannes Taubenböck · 2023
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“How much do large-scale and small-scale farming contribute to global deforestation? Results from a remote sensing pilot approach”, 2023, pp. 60
A Branthomme et al · 2023
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“Balancing the trade-offs between land productivity, labor productivity and labor intensity”
Cristina Chiarella, Patrick Meyfroidt, Dilini Abeygunawardane and Piero Conforti · 2023
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“Are Emergent Abilities of Large Language Models a Mirage?” arXiv:2304.15004 [cs]
Rylan Schaeffer, Brando Miranda and Sanmi Koyejo · 2023
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“Foundation Models for Generalist Geospatial Artificial Intelligence” Publisher: [object Object] Version Number: 2, 2023
Johannes Jakubik et al · 2023
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“Understanding the robustness of spectral-temporal metrics across the global Landsat archive from 1984 to 2019 – a quantitative evaluation”
David Frantz et al · 2023
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“AI Security for Geoscience and Remote Sensing: Challenges and future trends” Section: 60
Yonghao Xu et al · 2023
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“A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences”
Mara Graziani et al · 2023
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“Considerations for AI-EO for agriculture in Sub-Saharan Africa”
Catherine Nakalembe and Hannah Kerner · 2023
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“Ethics of Artificial Intelligence: Case Studies and Options for Addressing Ethical Challenges”
Bernd Stahl, Doris Schroeder and Rowena Rodrigues · 2023
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“A deep learning approach for deriving winter wheat phenology from optical and SAR time series at field level”
Felix Lobert et al · 2023
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“Taking it further: leveraging pseudo labels for field delineation across label-scarce smallholder regions” Publisher: [object Object] Version Number: 1, 2023
Philippe Rufin et al · 2023
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“Artificial intelligence to advance Earth observation: a perspective”
Devis Tuia et al · 2023
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“Environmental impacts of earth observation data in the constellation and cloud computing era”
R. Wilkinson et al · 2023
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“IPCC, 2022: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change”
H.-O. Portner · 2023
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“GreenGov.SG: Report for financial year 2022”
Ministry of Sustainability and the Environment · 2023
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“The ESA Green Transition Information Factories ? using Earth Observation and cloud-based analytics to address the Green Transition information needs”
P. Griffiths · 2023
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“Hazard and risk assessment for early phase road planning in Norway”
A. Solheim, K. Sverdrup-Thygeson and B. Kalsnes · 2023
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“Near-real-time Country-wide Estimation of Susceptibility and Settlement Exposure from Norwegian Mass Movements via Inter-graph Representation Learning”
J. Dimasaka, S. Selvakumaran and A. Marinoni · 2023
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“Seasonal forecasting skill for the High Mountain Asia region in the Goddard Earth Observing System”
E. Massoud · 2023
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“ESG Report 2023” [Accessed 22-05-2024], https://assets.planet.com/docs/Planet_Labs_PBC_2023_ESG_Report.pdf , 2023
PLANET PBC · 2023
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“Mission Critical–Satellite Data is a Distinct Modality in Machine Learning”
Esther Rolf, Konstantin Klemmer, Caleb Robinson and Hannah Kerner · 2024
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“Crossing Boundaries: The Ethics of AI and Geographic Information Technologies”
Isaac Oluoch · 2024
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“Mapping the diversity of land uses following deforestation across Africa”
Robert Masolele et al · 2024
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“Large Language Models are Geographically Biased” Publisher: [object Object] Version Number: 1, 2024
Rohin Manvi et al · 2024
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“National tree species mapping using Sentinel-1/2 time series and German National Forest Inventory data”
Lukas Blickensdörfer et al · 2024
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“Connecting Global Modes of Variability to Climate in High Mountain Asia”
E. Massoud, Y.-K. Lim, L. Andrews and M. Girotto · 2024
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“Sustainability — airbus.com” [Accessed 21-05-2024], https://www.airbus.com/en/sustainability
AIRBUS · 2024
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“AI For Good Lab - Microsoft Research — microsoft.com” [Accessed 21-05-2024], https://www.microsoft.com/en-us/research/group/ai-for-good-research-lab/
Microsoft · 2024
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“Sustainable Innovation & Technology - Google Sustainability — sustainability.google” [Accessed 21-05-2024], https://sustainability.google/
Google · 2024
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“For the planet and people: IBM’s focus on AI ethics in sustainability - IBM Blog — ibm.com” [Accessed 21-05-2024], https://www.ibm.com/blog/for-the-planet-and-people-ibms-focus-on-ai-ethics-in-sustainability/
IBM AI Board · 2024
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“Accelerating Geospatial Machine Learning — spacenet.ai” [Accessed 21-05-2024], https://spacenet.ai/
SpaceNet · 2024
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Microsoft · 2024
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“Inflation Reduction Act”
117th Congress of the United States America · 2090
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