{"id":"https://openalex.org/W7160960260","doi":"https://doi.org/10.48550/arxiv.2605.10789","title":"Rapid Forest Fuel Load Estimation via Virtual Remote Sensing and Metric-Scale Feed-Forward 3D Reconstruction","display_name":"Rapid Forest Fuel Load Estimation via Virtual Remote Sensing and Metric-Scale Feed-Forward 3D Reconstruction","publication_year":2026,"publication_date":"2026-05-11","ids":{"openalex":"https://openalex.org/W7160960260","doi":"https://doi.org/10.48550/arxiv.2605.10789"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.10789","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10789","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.10789","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135940989","display_name":"Quanyun Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Quanyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135914794","display_name":"Kyle Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Kyle","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135979799","display_name":"Wentao Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Wentao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135950931","display_name":"Zhengsen Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Zhengsen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123293499","display_name":"Hudson Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Hudson","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135961639","display_name":"Linlin Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Linlin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135996739","display_name":"Yuhao Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yuhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135992370","display_name":"David A. Clausi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Clausi, David A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135970755","display_name":"Jonathan Z. Li","orcid":"https://orcid.org/0000-0001-9914-9662"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jonathan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.8206999897956848,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.8206999897956848,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10555","display_name":"Fire effects on ecosystems","score":0.10729999840259552,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.017999999225139618,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.5745999813079834},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.5414000153541565},{"id":"https://openalex.org/keywords/leaf-area-index","display_name":"Leaf area index","score":0.46059998869895935},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.44839999079704285},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.43459999561309814},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.3790999948978424},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.37380000948905945},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.36660000681877136},{"id":"https://openalex.org/keywords/satellite-imagery","display_name":"Satellite imagery","score":0.3582000136375427}],"concepts":[{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.7398999929428101},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5770000219345093},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.5745999813079834},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.5414000153541565},{"id":"https://openalex.org/C25989453","wikidata":"https://www.wikidata.org/wiki/Q446746","display_name":"Leaf area index","level":2,"score":0.46059998869895935},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.44839999079704285},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.43459999561309814},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.41839998960494995},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3790999948978424},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.37380000948905945},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.36660000681877136},{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.3582000136375427},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3528999984264374},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3375999927520752},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C39807119","wikidata":"https://www.wikidata.org/wiki/Q1134228","display_name":"Tree canopy","level":3,"score":0.31839999556541443},{"id":"https://openalex.org/C147103442","wikidata":"https://www.wikidata.org/wiki/Q1423188","display_name":"Forest inventory","level":3,"score":0.3172000050544739},{"id":"https://openalex.org/C39399123","wikidata":"https://www.wikidata.org/wiki/Q1348989","display_name":"Earth observation","level":3,"score":0.3070000112056732},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2856999933719635},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C2778405918","wikidata":"https://www.wikidata.org/wiki/Q1082970","display_name":"Reducing emissions from deforestation and forest degradation","level":4,"score":0.2797999978065491},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2644999921321869},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25940001010894775},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.10789","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10789","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.10789","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10789","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.7135608196258545}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"quantification":[1],"of":[2,101,197],"forest":[3,55,198],"coverage":[4],"and":[5,16,31,75,148,175],"combustible":[6],"biomass":[7,199],"(fuel":[8],"load)":[9],"is":[10,140],"critical":[11],"for":[12,42,53,78],"wildfire":[13],"risk":[14],"assessment":[15],"ecosystem":[17],"management.":[18],"However,":[19],"traditional":[20],"methods":[21],"relying":[22],"on":[23],"airborne":[24],"LiDAR":[25],"or":[26],"field":[27],"surveys":[28],"are":[29],"cost-prohibitive":[30],"time-intensive,":[32],"while":[33],"satellite":[34],"imagery":[35,74],"often":[36],"lacks":[37],"the":[38,91,102,109,124],"vertical":[39],"resolution":[40],"required":[41],"canopy":[43],"volume":[44],"analysis.":[45],"This":[46,94],"paper":[47],"proposes":[48],"a":[49,79,98,118,154,187],"novel,":[50],"automated":[51],"pipeline":[52,185],"rapid":[54],"inventory":[56],"using":[57],"virtual":[58],"remote":[59],"sensing":[60],"data":[61],"derived":[62],"from":[63],"Google":[64],"Earth":[65],"Studio":[66],"(GES).":[67],"Our":[68],"approach":[69],"first":[70],"generates":[71],"low-altitude":[72],"orbital":[73],"camera":[76],"poses":[77,131],"target":[80],"region.":[81],"For":[82],"dense":[83],"3D":[84],"reconstruction,":[85,115],"we":[86,116,152],"employ":[87,153],"Pi-Long,":[88],"developed":[89],"within":[90],"VGGT-Long":[92],"framework.":[93],"model":[95],"serves":[96],"as":[97],"scalable":[99],"extension":[100],"Pi-3":[103],"feed-forward":[104],"Transformer":[105],"architecture.":[106],"To":[107],"address":[108],"inherent":[110],"scale":[111],"ambiguity":[112],"in":[113],"monocular":[114],"introduce":[117],"metric":[119],"recovery":[120],"module":[121],"that":[122,183],"aligns":[123],"reconstructed":[125],"trajectory":[126],"with":[127,159,200],"GES":[128],"ground":[129],"truth":[130],"via":[132],"Sim(3)":[133],"Umeyama":[134],"optimization.":[135],"The":[136],"metric-scale":[137],"point":[138],"cloud":[139],"then":[141],"orthogonally":[142],"projected":[143],"into":[144],"Bird's-Eye-View":[145],"(BEV)":[146],"height":[147,160],"density":[149],"maps.":[150],"Finally,":[151],"watershed-based":[155],"segmentation":[156],"algorithm":[157],"combined":[158],"variance":[161],"analysis":[162],"to":[163,191],"classify":[164],"tree":[165],"species":[166],"(conifer":[167],"vs.":[168],"broadleaf),":[169],"calculate":[170],"Leaf":[171],"Area":[172],"Index":[173],"(LAI),":[174],"estimate":[176],"total":[177],"fuel":[178],"load.":[179],"Experimental":[180],"results":[181],"demonstrate":[182],"this":[184],"offers":[186],"scalable,":[188],"cost-effective":[189],"alternative":[190],"physical":[192],"scanning,":[193],"enabling":[194],"near-real-time":[195],"estimation":[196],"high":[201],"geometric":[202],"consistency.":[203]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}
