{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T05:16:02Z","timestamp":1730265362852,"version":"3.28.0"},"reference-count":21,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,7,7]],"date-time":"2024-07-07T00:00:00Z","timestamp":1720310400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,7,7]],"date-time":"2024-07-07T00:00:00Z","timestamp":1720310400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,7]]},"DOI":"10.1109\/igarss53475.2024.10641065","type":"proceedings-article","created":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T17:56:13Z","timestamp":1725558973000},"page":"7483-7487","source":"Crossref","is-referenced-by-count":0,"title":["Geospatial Sampling by Maximizing Information Entropy"],"prefix":"10.1109","author":[{"given":"Daiki","family":"Kimura","sequence":"first","affiliation":[{"name":"IBM Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naomi","family":"Simumba","sequence":"additional","affiliation":[{"name":"IBM Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcus","family":"Freitag","sequence":"additional","affiliation":[{"name":"IBM Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johannes","family":"Schmude","sequence":"additional","affiliation":[{"name":"IBM Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michiaki","family":"Tatsubori","sequence":"additional","affiliation":[{"name":"IBM Research"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"journal-title":"Intergovernmental Panel on Climate Change","article-title":"Global warming of 1.5\u00b0c","year":"2018","key":"ref1"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aah3443"},{"issue":"5","key":"ref3","first-page":"689","article-title":"Environmental sustainability: Moving beyond business as usual","volume":"60","author":"Gibson","year":"2017","journal-title":"Business Horizons"},{"issue":"1","key":"ref4","first-page":"1","article-title":"Big earth data analytics for climate resilience","volume":"3","author":"Palumbo","year":"2019","journal-title":"Big Earth Data"},{"key":"ref5","first-page":"4437","article-title":"Big earth data cube processing in the cloud","volume":"12","author":"Chen","year":"2019","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"issue":"4","key":"ref6","first-page":"232","article-title":"Machine learning for geospatial data: from research to practice","volume":"9","author":"Wang","year":"2020","journal-title":"ISPRS International Journal of Geo-Information"},{"issue":"1","key":"ref7","first-page":"50","article-title":"Deep learning for remote sensing data: A technical tutorial on the state of the art","volume":"9","author":"Wang","year":"2021","journal-title":"IEEE Geoscience and Remote Sensing Magazine"},{"journal-title":"Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training","year":"2022","author":"Tong","key":"ref8"},{"key":"ref9","first-page":"197","article-title":"Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery","volume":"35","author":"Cong","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"journal-title":"Foundation Models for Generalist Geospatial Artificial Intelligence","year":"2023","author":"Jakubik","key":"ref10"},{"key":"ref11","article-title":"Huggingface page for prithvi model"},{"article-title":"Area sampling for training geospatial foundation models","volume-title":"American Geophysical Union Fall Meeting","author":"Kimura","key":"ref12"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2018.09.002"},{"key":"ref14","article-title":"Sampling methods for geographical research","author":"Dixon","year":"1978","journal-title":"Geo Abstracts"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.fecs.2023.100104"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-30162-4_478"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2011.08.026"},{"key":"ref18","article-title":"Sentinel-2 esa\u2019s optical high-resolution mission for gmes operational services","volume":"SP-1322","author":"Bertini","year":"2012","journal-title":"ESA bulletin. Bulletin ASE. European Space Agency"},{"key":"ref19","article-title":"Hls subsetting, processing, and exporting reformatted data prep script"},{"key":"ref20","article-title":"Efficient querying using overview layers of geospatial-temporal data in a data analytics platform","volume-title":"US Patent","author":"Freitag","year":"2022"},{"key":"ref21","article-title":"Prism"}],"event":{"name":"IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium","start":{"date-parts":[[2024,7,7]]},"location":"Athens, Greece","end":{"date-parts":[[2024,7,12]]}},"container-title":["IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10640349\/10640352\/10641065.pdf?arnumber=10641065","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T07:21:19Z","timestamp":1725607279000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10641065\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,7]]},"references-count":21,"URL":"https:\/\/doi.org\/10.1109\/igarss53475.2024.10641065","relation":{},"subject":[],"published":{"date-parts":[[2024,7,7]]}}}