{"id":"https://openalex.org/W7221496774","doi":null,"title":"Geospatial Understanding of Foundation Models: Self-Supervision, Robustness, and Resource-Constrained Learning","display_name":"Geospatial Understanding of Foundation Models: Self-Supervision, Robustness, and Resource-Constrained Learning","publication_year":2026,"publication_date":"2026-03-26","ids":{"openalex":"https://openalex.org/W7221496774"},"language":"en","primary_location":{"id":"pmh:oai:digitalcommons.usf.edu:etd-12704","doi":null,"is_oa":false,"landing_page_url":"https://digitalcommons.usf.edu/etd/11401","pdf_url":null,"source":{"id":"https://openalex.org/S4377196272","display_name":"Digital Commons - University of South Florida (University of South Florida)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2613432","host_organization_name":"University of South Florida","host_organization_lineage":["https://openalex.org/I2613432"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"USF Tampa Graduate Theses and Dissertations","raw_type":"dissertation"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5158369870","display_name":"Gilbert Kibet Rotich","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Rotich, Gilbert Kibet","raw_affiliation_strings":["University of South Florida"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Florida","institution_ids":["https://openalex.org/I2613432"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5158369870"],"corresponding_institution_ids":["https://openalex.org/I2613432"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9541000127792358,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9541000127792358,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.03020000085234642,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.008899999782443047,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/geospatial-foundation-models","display_name":"geospatial foundation models","score":0.9240000247955322},{"id":"https://openalex.org/keywords/satellite-imagery","display_name":"satellite imagery","score":0.7260000109672546},{"id":"https://openalex.org/keywords/self-supervised-pretraining","display_name":"self-supervised pre-training","score":0.7950000166893005},{"id":"https://openalex.org/keywords/multispectral-imagery","display_name":"multispectral imagery","score":0.7279999852180481},{"id":"https://openalex.org/keywords/domain-shift","display_name":"domain shift","score":0.4880000054836273},{"id":"https://openalex.org/keywords/transfer-learning","display_name":"transfer learning","score":0.5429999828338623},{"id":"https://openalex.org/keywords/vit","display_name":"ViT","score":0.3799999952316284},{"id":"https://openalex.org/keywords/change-detection","display_name":"change detection","score":0.6389999985694885},{"id":"https://openalex.org/keywords/oriented-object-detection","display_name":"oriented object detection","score":0.9589999914169312},{"id":"https://openalex.org/keywords/semantic-segmentation","display_name":"semantic segmentation","score":0.6499999761581421},{"id":"https://openalex.org/keywords/knowledge-distillation","display_name":"knowledge distillation","score":0.890999972820282},{"id":"https://openalex.org/keywords/resource-constrained-learning","display_name":"resource-constrained learning","score":0.8550000190734863},{"id":"https://openalex.org/keywords/bigearthnet","display_name":"BigEarthNet","score":0.5680000185966492},{"id":"https://openalex.org/keywords/eurosat","display_name":"EuroSAT","score":0.699999988079071}],"concepts":[{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.8938000202178955},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.671500027179718},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6524999737739563},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6105999946594238},{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.5026000142097473},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47999998927116394},{"id":"https://openalex.org/C61272859","wikidata":"https://www.wikidata.org/wiki/Q7834031","display_name":"Transferability","level":3,"score":0.4722000062465668},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4535999894142151},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4462999999523163},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.44040000438690186},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.43299999833106995},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4253000020980835},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.421999990940094},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3885999917984009},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.3693000078201294},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.3617999851703644},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3447999954223633},{"id":"https://openalex.org/C196031653","wikidata":"https://www.wikidata.org/wiki/Q1501867","display_name":"Cartographic generalization","level":3,"score":0.33390000462532043},{"id":"https://openalex.org/C41856607","wikidata":"https://www.wikidata.org/wiki/Q483130","display_name":"Geographic information system","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2865000069141388},{"id":"https://openalex.org/C39399123","wikidata":"https://www.wikidata.org/wiki/Q1348989","display_name":"Earth observation","level":3,"score":0.2824000120162964},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.27399998903274536},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.27250000834465027},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C104541649","wikidata":"https://www.wikidata.org/wiki/Q6935090","display_name":"Multispectral pattern recognition","level":3,"score":0.2712000012397766},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.257099986076355},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2565000057220459},{"id":"https://openalex.org/C2780896845","wikidata":"https://www.wikidata.org/wiki/Q17152984","display_name":"Authentic learning","level":2,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:digitalcommons.usf.edu:etd-12704","doi":null,"is_oa":false,"landing_page_url":"https://digitalcommons.usf.edu/etd/11401","pdf_url":null,"source":{"id":"https://openalex.org/S4377196272","display_name":"Digital Commons - University of South Florida (University of South Florida)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2613432","host_organization_name":"University of South Florida","host_organization_lineage":["https://openalex.org/I2613432"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"USF Tampa Graduate Theses and Dissertations","raw_type":"dissertation"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4411406789","https://openalex.org/W7213551641","https://openalex.org/W4395055639","https://openalex.org/W4416728495","https://openalex.org/W7204906186","https://openalex.org/W4390872394","https://openalex.org/W7167358387","https://openalex.org/W4387829118","https://openalex.org/W4415961092","https://openalex.org/W7133235565"],"abstract_inverted_index":{"Research":[0],"on":[1,16,26,32,97,180,191,332,342,360],"large-scale":[2],"geospatial":[3,148],"understanding":[4],"from":[5,244,268,299,349],"satellite":[6,71,91,375],"imagery":[7,243],"remains":[8],"relativelyunderexplored.":[9],"Much":[10],"of":[11,89,132,147],"the":[12,86,133,169,333,338,350,355],"field":[13],"still":[14],"relies":[15,331],"narrow,":[17],"task-specific":[18],"pipelines,":[19],"and":[20,45,54,80,99,124,145,173,200,205,224,231,235,255,266,296,322,357,362,384,392],"there":[21],"is":[22,211,238,340],"lim-":[23],"ited":[24],"work":[25],"developing":[27],"true":[28],"foundation":[29,149,159,215,303],"models":[30,38,69,111,150,216,378],"or":[31,184,218],"systematically":[33],"evaluating":[34],"how":[35],"well":[36],"these":[37],"generalize":[39],"under":[40],"domain":[41],"shift,":[42],"geographic":[43],"variation,":[44],"real-world":[46],"scene":[47,256],"diversity.":[48],"This":[49],"dissertation":[50],"advances":[51],"foundation-model":[52],"pretraining":[53,249,291,336],"evaluation":[55],"for":[56,70,104,112,222,354],"remote":[57],"sensing":[58],"along":[59],"three":[60],"complementary":[61],"axes.":[62],"First,":[63],"we":[64,154,283],"develop":[65],"self-supervised":[66,162],"multispectral":[67,83,109],"pretrained":[68,110,179,341],"image":[72,134],"classification":[73,105,223,361],"by":[74,240],"adapting":[75],"BYOL,":[76],"BarlowTwins,":[77],"DINO,":[78],"MoCo,":[79],"Simsiam":[81],"to":[82,94,117,142,270,279,305],"inputs,":[84],"addressing":[85],"limited":[87],"amount":[88],"curated":[90],"datasets":[92],"relative":[93],"ImageNet.":[95],"Pretraining":[96,176],"Eurosat":[98],"BigEarthNet":[100,113],"yields":[101,272],"strong":[102],"transfer":[103],"across":[106,312],"datasets,":[107],"providing":[108],"that":[114,209,293,330],"have":[115],"yet":[116,226],"be":[118],"fully":[119],"explored":[120],"in":[121,138,242],"prior":[122],"work,":[123],"demonstrating":[125],"competitive":[126],"performance":[127,210,237,369],"despite":[128],"using":[129],"a":[130,275,290,300,306,343],"fraction":[131],"samples":[135],"typically":[136],"used":[137,353],"ImageNet-scale":[139],"training.":[140],"Second,":[141],"study":[143],"robustness":[144],"generalization":[146],"beyond":[151],"narrow":[152],"demonstrations,":[153],"benchmark":[155],"eight":[156],"state-of-the-art":[157,374],"ViT-based":[158],"models,":[160],"spanning":[161],"MAE":[163],"variants":[164],"(SatMAE,":[165],"Scale-MAE,":[166],"Cross-Scale":[167],"MAE),":[168],"Geospatial":[170],"Foundation":[171],"Model,":[172],"supervised":[174,220,327],"Multi-Task":[175],"(MTP).":[177],"Models":[178],"fMoW,":[181],"SAMRS,":[182],"Million-AID,":[183],"GeoPile":[185],"are":[186,394],"evaluated":[187],"via":[188,309],"end-to-end":[189],"fine-tuning":[190,385],"four":[192],"down-":[193],"stream":[194],"tasks\u2014classification,":[195],"segmentation,":[196,225],"oriented":[197,232],"object":[198,233],"detection,":[199,234],"change":[201,229],"detection\u2014across":[202],"DOTA-v2.0,":[203],"BigEarthNet,":[204],"xDB.":[206],"Results":[207],"show":[208],"strongly":[212],"task":[213],"dependent:":[214],"match":[217],"exceed":[219],"baselines":[221],"struggle":[227],"with":[228,372],"detection":[230],"cross-dataset":[236],"hampered":[239],"differences":[241],"different":[245],"satellites.":[246],"Across":[247],"settings,":[248],"data":[250],"quality,":[251],"particularly":[252],"spatial":[253,295,317],"resolution":[254],"diversity,":[257],"drives":[258],"downstream":[259],"success":[260],"more":[261],"than":[262,274],"sheer":[263],"dataset":[264,346,352],"size,":[265],"scaling":[267],"ViT-Base":[269],"ViT-Large":[271],"less":[273],"1%":[276],"improvement.":[277],"Finally,":[278],"enable":[280],"resource-constrained":[281],"learning,":[282],"introduce":[284],"Hierarchical":[285],"Attention":[286],"Dis-":[287],"tillation":[288],"(HAD),":[289],"paradigm":[292],"transfers":[294],"semantic":[297],"representations":[298],"large":[301],"teacher":[302],"model":[304,367],"compact":[307,366],"student":[308,339],"multi-level":[310],"attention":[311],"feature":[313],"maps":[314],"at":[315],"multiple":[316],"scales,":[318],"improving":[319,381],"feature-transfer":[320],"fidelity":[321],"accelerating":[323],"convergence.":[324],"Unlike":[325],"conventional":[326],"knowledge":[328],"distillation":[329],"teacher\u2019s":[334],"labeled":[335],"data,":[337],"separate":[344],"unlabeled":[345],"(SSL4EO),":[347],"distinct":[348],"MillionAID":[351],"teacher,":[356],"then":[358],"fine-tuned":[359],"segmentation.":[363],"The":[364],"distilled":[365],"achieves":[368],"closely":[370],"aligned":[371],"current":[373],"founda-":[376],"tion":[377],"while":[379],"substantially":[380],"computational":[382],"efficiency":[383],"time,":[386],"supporting":[387],"practical":[388],"deployment":[389],"where":[390],"parameters":[391],"latency":[393],"constrained":[395]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-10-12T00:00:00"}