[{"data":1,"prerenderedAt":649},["ShallowReactive",2],{"method-shinemapping2023":3},{"method":4,"reference":52,"equipment":73,"figures":90,"results":91},{"id":5,"label":6,"shortName":7,"title":8,"year":9,"era":10,"cluster":11,"scope":12,"keyIdeaZh":13,"keyIdeaEn":14,"fulltextStatus":15,"publicationStatus":16,"recommendation":17,"constructionRelevance":18,"validationEnvironment":19,"strengths":23,"limitations":26,"sensors":32,"platform":34,"estimator":37,"association":38,"timeModel":39,"deskew":39,"loopClosure":40,"globalOptimization":40,"mapRepresentation":41,"prior":42,"outputGeometry":43,"compute":44,"codeUrl":45,"codeLicense":46,"relatedVersions":47},"shinemapping2023","Zhong et al., 2023","SHINE-Mapping","SHINE-Mapping: Large-Scale 3D Mapping Using Sparse Hierarchical Implicit Neural Representations",2023,"recent","C09","map_representation_or_reconstruction","SHINE-Mapping 以稀疏八元樹階層特徵格網搭配共用淺層 MLP，從已知位姿的 LiDAR 點雲學習符號距離場（SDF），並以正則化處理增量建圖的遺忘問題。它不做位姿估計，屬已知位姿下的建圖元件。作者用合成 MaiCity 與具 TLS 參考網格的 Newer College 評估，並以所有比較方法重建交集遮罩後的參考網格計算精度。","Learns an SDF in a sparse hierarchical octree of neural features from posed LiDAR scans for memory-efficient large-scale mapping.","full_text_reviewed","peer_reviewed_published","background","論文未在營建場域測試；室內示例為作者實驗室樓層（IPB office）。",[20,21,22],"simulation","public_benchmark","independent_reference",[24,25],"More accurate, complete and memory-efficient reconstructions than Voxblox, VDB Fusion and Puma (Poisson-surface-based) on MaiCity\u002FNewer College (abstract; Sec. IV-B; Tables II-III)","Reasonable scene completion in occluded indoor areas without observations (Sec. IV-E)",[27,28,29,30,31],"Requires known poses (Sec. III)","Incremental mode depends on a fixed pre-trained decoder to limit forgetting (Sec. III-A)","Accuracy metric computed on a GT mesh masked by the intersection of all reconstructions (Sec. IV-B), which limits comparability with other papers (inference)","No runtime or hardware is reported, so the online incremental claim is not quantified (whole paper; inference)","Large-scale (KITTI, about 4 km) and forgetting results are qualitative only (Sec. IV-D; Fig. 1; Fig. 8)",[33],"3D LiDAR",[35,36],"vehicle","handheld","not_applicable (mapping with known poses)","SDF supervision from range measurements (binary cross-entropy on samples along rays)","not_applicable","none","sparse octree-based hierarchical feature grid with a shared shallow MLP decoding SDF","known sensor poses; for incremental mapping a pre-trained MLP is kept fixed","mesh via marching cubes on a fixed grid","not reported: no hardware, runtime or frame rate is given; only map memory (Fig. 7 plot; 84 MB for an indoor floor at 3 cm leaf resolution, Sec. IV-E)","https:\u002F\u002Fgithub.com\u002FPRBonn\u002FSHINE_mapping","MIT",[48],{"relation":49,"title":50,"doi_or_url":51},"preprint","arXiv:2210.02299","https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.02299",{"id":5,"kind":53,"shortName":7,"title":8,"authors":54,"year":9,"venue":59,"venueType":60,"publisher":61,"volumeIssuePages":62,"doi":63,"arxivId":64,"url":65,"firstPublicDate":66,"publicationStatus":16,"metadataStatus":67,"fulltextStatus":15,"era":10,"classicReason":39,"codeUrl":45,"cluster":11,"topics":68,"mdpi":69,"verification":70,"label":6,"fulltextRoute":71,"versionRead":72,"addedByCensus":69},"method",[55,56,57,58],"Xingguang Zhong","Yue Pan","Jens Behley","Cyrill Stachniss","2023 IEEE International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 8371-8377","10.1109\u002Ficra48891.2023.10160907","2210.02299","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002Ficra48891.2023.10160907","2022-10-05","metadata_verified",[11],false,"corrected","arXiv","arXiv v3 (20 Feb 2023) read in full; IEEE version of record PDF (ICRA 2023, pp. 8371-8377) obtained via NTU and checked: same 7-page content and identical table values",[74,81,85],{"category":75,"model":76,"canonical":76,"role":77,"dataset":78,"specs":79,"locator":80},"lidar","64-beam LiDAR (simulated, noise-free)","dataset sensor","MaiCity","Synthetic scans of an urban scenario","Sec. IV-A",{"category":75,"model":82,"canonical":82,"role":77,"dataset":83,"specs":84,"locator":80},"handheld LiDAR (model not stated in the paper)","Newer College","cm-level measurement noise and substantial motion distortion",{"category":86,"model":87,"canonical":87,"role":88,"dataset":83,"specs":89,"locator":80},"tls_scanner","terrestrial scanner (model not stated in the paper)","reference or ground truth","Provides the near ground truth mesh",[],{"totalRows":92,"groupCount":93,"groups":94,"others":632},60,7,[95,289,435,558],{"slug":96,"group":97,"sourceId":98,"sourceLabel":99,"table":100,"selfRows":101,"metrics":102,"seqs":115,"entrants":128,"cells":142,"outcomes":281,"locators":284,"hardware":285,"wordings":286,"notes":287},"loner2023-table-iii","loner2023:Table III","loner2023","Isaacson et al., 2023","Table III",16,[103,108,110,113],{"label":104,"unit":105,"statistic":106,"alignment":107},"Accuracy (mean distance estimated to ground truth)","m","mean","not_reported",{"label":109,"unit":105,"statistic":106,"alignment":107},"Completion (mean distance ground truth to estimated)",{"label":111,"unit":112,"statistic":107,"alignment":107},"Precision at 0.1 m threshold","ratio",{"label":114,"unit":112,"statistic":107,"alignment":107},"Recall at 0.1 m threshold",[116,120,123,125],{"dataset":117,"sequence":118,"environment":119},"Fusion Portable","MCR Slow 01","indoor lab, quadruped",{"dataset":117,"sequence":121,"environment":122},"Canteen Day","semi-outdoor courtyard, handheld",{"dataset":117,"sequence":124,"environment":122},"Garden Day",{"dataset":83,"sequence":126,"environment":127},"Quad Easy","large outdoor college quad, two laps",[129,133,135,138,140],{"name":130,"methodId":131,"linkable":132,"proposed":69,"self":69},"NICE-SLAM","niceslam2022",true,{"name":134,"methodId":5,"linkable":132,"proposed":69,"self":132},"SHINE (ground-truth poses)",{"name":136,"methodId":137,"linkable":69,"proposed":69,"self":69},"LONER w.\u002F L_CLONeR",null,{"name":139,"methodId":137,"linkable":69,"proposed":69,"self":69},"LONER w.\u002F L_URF",{"name":141,"methodId":98,"linkable":132,"proposed":132,"self":69},"LONER",[143,147,150,153,156,159,161,163,165,167,169,171,173,175,177,179,181,183,185,187,189,190,191,192,193,194,195,197,199,201,203,204,205,206,207,208,209,211,213,215,217,218,219,220,221,222,223,225,227,229,231,232,233,234,235,236,237,239,241,243,245,246,248,250,252,254,255,257,259,261,263,264,266,268,270,272,273,275,277,279],[144,144,144,145,146,144,146,146,144],0,0.621,-1,[148,144,144,149,146,144,146,146,144],1,0.164,[151,144,144,152,146,144,146,146,144],2,0.11,[154,144,144,155,146,144,146,146,144],3,0.153,[157,144,144,158,146,144,146,146,144],4,0.186,[144,148,144,160,146,144,146,146,144],0.419,[148,148,144,162,146,144,146,146,144],0.075,[151,148,144,164,146,144,146,146,144],0.08,[154,148,144,166,146,144,146,146,144],0.102,[157,148,144,168,146,144,146,146,144],0.069,[144,151,144,170,146,144,146,146,144],0.124,[148,151,144,172,146,144,146,146,144],0.624,[151,151,144,174,146,144,146,146,144],0.665,[154,151,144,176,146,144,146,146,144],0.449,[157,151,144,178,146,144,146,146,144],0.473,[144,154,144,180,146,144,146,146,144],0.476,[148,154,144,182,146,144,146,146,144],0.757,[151,154,144,184,146,144,146,146,144],0.94,[154,154,144,186,146,144,146,146,144],0.884,[157,154,144,188,146,144,146,146,144],0.932,[144,144,148,137,144,144,146,146,144],[148,144,148,137,148,144,146,146,144],[151,144,148,137,148,144,146,146,144],[154,144,148,137,148,144,146,146,144],[157,144,148,137,148,144,146,146,144],[144,148,148,137,144,144,146,146,144],[148,148,148,196,146,144,146,146,144],0.116,[151,148,148,198,146,144,146,146,144],0.22,[154,148,148,200,146,144,146,146,144],0.19,[157,148,148,202,146,144,146,146,144],0.105,[144,151,148,137,144,144,146,146,144],[148,151,148,137,148,144,146,146,144],[151,151,148,137,148,144,146,146,144],[154,151,148,137,148,144,146,146,144],[157,151,148,137,148,144,146,146,144],[144,154,148,137,144,144,146,146,144],[148,154,148,210,146,144,146,146,144],0.753,[151,154,148,212,146,144,146,146,144],0.524,[154,154,148,214,146,144,146,146,144],0.846,[157,154,148,216,146,144,146,146,144],0.878,[144,144,151,137,144,144,146,146,144],[148,144,151,137,148,144,146,146,144],[151,144,151,137,148,144,146,146,144],[154,144,151,137,148,144,146,146,144],[157,144,151,137,148,144,146,146,144],[144,148,151,137,144,144,146,146,144],[148,148,151,224,146,144,146,146,144],0.13,[151,148,151,226,146,144,146,146,144],0.333,[154,148,151,228,146,144,146,146,144],0.539,[157,148,151,230,146,144,146,146,144],0.157,[144,151,151,137,144,144,146,146,144],[148,151,151,137,148,144,146,146,144],[151,151,151,137,148,144,146,146,144],[154,151,151,137,148,144,146,146,144],[157,151,151,137,148,144,146,146,144],[144,154,151,137,144,144,146,146,144],[148,154,151,238,146,144,146,146,144],0.657,[151,154,151,240,146,144,146,146,144],0.469,[154,154,151,242,146,144,146,146,144],0.623,[157,154,151,244,146,144,146,146,144],0.784,[144,144,154,137,148,144,146,146,144],[148,144,154,247,146,144,146,146,144],0.301,[151,144,154,249,146,144,146,146,144],0.663,[154,144,154,251,146,144,146,146,144],0.552,[157,144,154,253,146,144,146,146,144],0.38,[144,148,154,137,148,144,146,146,144],[148,148,154,256,146,144,146,146,144],0.148,[151,148,154,258,146,144,146,146,144],0.543,[154,148,154,260,146,144,146,146,144],0.895,[157,148,154,262,146,144,146,146,144],0.373,[144,151,154,137,148,144,146,146,144],[148,151,154,265,146,144,146,146,144],0.453,[151,151,154,267,146,144,146,146,144],0.15,[154,151,154,269,146,144,146,146,144],0.127,[157,151,154,271,146,144,146,146,144],0.327,[144,154,154,137,148,144,146,146,144],[148,154,154,274,146,144,146,146,144],0.717,[151,154,154,276,146,144,146,146,144],0.602,[154,154,154,278,146,144,146,146,144],0.484,[157,154,154,280,146,144,146,146,144],0.809,[282,283],"failed","not_applicable (invalid configuration '-')",[100],[],[],[288],"Map accuracy and completion (m, mean nearest-point distances) and precision and recall at a 0.1 m threshold; meshes from each method sampled to point clouds, all clouds voxel-downsampled to 5 cm (1 cm for MCR), ground truth cropped to geometry observed by the sensor. SHINE Mapping used ground-truth poses. '-' = invalid configuration (accuracy and precision are '-' for every method on Canteen and Garden; NICE-SLAM '-' on Quad), x = failed (NICE-SLAM on Canteen and Garden, all metrics).",{"slug":290,"group":291,"sourceId":292,"sourceLabel":293,"table":294,"selfRows":295,"metrics":296,"seqs":311,"entrants":317,"cells":331,"outcomes":428,"locators":429,"hardware":431,"wordings":432,"notes":433},"zhu2025meshloam-table-v","zhu2025meshloam:Table V","zhu2025meshloam","Zhu et al., 2025","Table V",10,[297,300,302,304,307,309],{"label":298,"unit":299,"statistic":107,"alignment":40},"Comp. (cm), completion","cm",{"label":301,"unit":299,"statistic":107,"alignment":40},"Acc. (cm), accuracy",{"label":303,"unit":299,"statistic":107,"alignment":40},"C-L1 (cm), Chamfer-L1 distance",{"label":305,"unit":306,"statistic":107,"alignment":40},"Comp.Ratio (%)","%",{"label":308,"unit":306,"statistic":107,"alignment":40},"F-score (10cm) (%)",{"label":310,"unit":306,"statistic":107,"alignment":40},"F-score (20cm) (%)",[312,315],{"dataset":313,"sequence":313,"environment":314},"Mai City","simulated urban street",{"dataset":83,"sequence":107,"environment":316},"outdoor college (handheld)",[318,321,324,326,329],{"name":319,"methodId":320,"linkable":132,"proposed":69,"self":69},"VDB Fusion [28]","vizzo2022vdbfusion",{"name":322,"methodId":323,"linkable":132,"proposed":69,"self":69},"Puma [13]","vizzo2021puma",{"name":325,"methodId":5,"linkable":132,"proposed":69,"self":132},"SHINE-Mapping [30]",{"name":327,"methodId":328,"linkable":132,"proposed":69,"self":69},"SLAMesh [14]","ruan2023slamesh",{"name":330,"methodId":292,"linkable":132,"proposed":132,"self":69},"Ours",[332,334,336,338,340,342,344,345,347,349,352,354,356,358,360,362,364,366,368,370,372,374,376,378,380,382,383,385,387,389,391,393,395,397,399,401,403,405,407,409,411,413,414,416,418,420,422,423,425,427],[144,144,144,333,146,144,146,146,144],6.9,[144,148,144,335,146,144,146,146,144],1.3,[144,151,144,337,146,144,146,146,144],4.5,[144,154,144,339,146,144,146,146,144],90.2,[144,157,144,341,146,144,146,146,144],94.1,[144,144,148,343,146,144,146,146,144],12,[144,148,148,333,146,144,146,146,144],[144,151,148,346,146,144,146,146,144],9.4,[144,154,148,348,146,144,146,146,144],91.3,[144,350,148,351,146,144,146,146,144],5,92.6,[148,144,144,353,146,144,146,146,144],32,[148,148,144,355,146,144,146,146,144],1.2,[148,151,144,357,146,144,146,146,144],16.9,[148,154,144,359,146,144,146,146,144],78.8,[148,157,144,361,146,144,146,146,144],87.3,[148,144,148,363,146,144,146,146,144],15.4,[148,148,148,365,146,144,146,146,144],7.7,[148,151,148,367,146,144,146,146,144],11.5,[148,154,148,369,146,144,146,146,144],89.9,[148,350,148,371,146,144,146,146,144],91.9,[151,144,144,373,146,144,146,146,144],3.2,[151,148,144,375,146,144,146,146,144],1.1,[151,151,144,377,146,144,146,146,144],2.9,[151,154,144,379,146,144,146,146,144],95.2,[151,157,144,381,146,144,146,146,144],95.9,[151,144,148,295,146,144,146,146,144],[151,148,148,384,146,144,146,146,144],6.7,[151,151,148,386,146,144,146,146,144],8.4,[151,154,148,388,146,144,146,146,144],93.6,[151,350,148,390,146,144,146,146,144],93.7,[154,144,144,392,146,144,146,146,144],7.5,[154,148,144,394,146,144,146,146,144],3.7,[154,151,144,396,146,144,146,146,144],6.1,[154,154,144,398,146,144,146,146,144],89.2,[154,157,144,400,146,144,146,146,144],90.6,[154,144,148,402,146,144,146,146,144],13.7,[154,148,148,404,146,144,146,146,144],11.4,[154,151,148,406,146,144,146,146,144],12.6,[154,154,148,408,146,144,146,146,144],83.5,[154,350,148,410,146,144,146,146,144],82.3,[157,144,144,412,146,144,146,146,144],2.5,[157,148,144,355,146,144,146,146,144],[157,151,144,415,146,144,146,146,144],2.4,[157,154,144,417,146,144,146,146,144],96.3,[157,157,144,419,146,144,146,146,144],97.4,[157,144,148,421,146,144,146,146,144],9.6,[157,148,148,384,146,144,146,146,144],[157,151,148,424,146,144,146,146,144],8.2,[157,154,148,426,146,144,146,146,144],94.2,[157,350,148,341,146,144,146,146,144],[],[430],"Table V (VoR)",[],[],[434],"Mesh quality with ground-truth poses for all methods, voxel size 0.1 m, settings of SHINE-Mapping; distances in cm; completion ratio and F-score in % at 10 cm (Mai City) and 20 cm (Newer College)",{"slug":436,"group":437,"sourceId":438,"sourceLabel":439,"table":440,"selfRows":441,"metrics":442,"seqs":454,"entrants":460,"cells":471,"outcomes":552,"locators":553,"hardware":554,"wordings":555,"notes":556},"nerfloam2023-table-1","nerfloam2023:Table 1","nerfloam2023","Deng et al., 2023","Table 1",8,[443,446,448,450,452],{"label":444,"unit":445,"statistic":107,"alignment":107},"Map. Acc.","cm (not stated in this table; identical values are labelled cm in PIN-SLAM Table XI)",{"label":447,"unit":445,"statistic":107,"alignment":107},"Map. Comp.",{"label":449,"unit":445,"statistic":107,"alignment":107},"C-l1 (Chamfer-L1)",{"label":451,"unit":306,"statistic":107,"alignment":107},"F-score (10cm)",{"label":453,"unit":306,"statistic":107,"alignment":107},"F-score (20cm)",[455,457],{"dataset":78,"sequence":107,"environment":456},"synthetic urban street (simulated 64-beam LiDAR)",{"dataset":83,"sequence":458,"environment":459},"not_reported (Newer College sequence not named; one of every five scans used)","outdoor campus, hand-carried LiDAR",[461,463,465,467,469],{"name":462,"methodId":5,"linkable":132,"proposed":69,"self":132},"SHINE [50] with KissICP poses",{"name":464,"methodId":320,"linkable":132,"proposed":69,"self":69},"Vdbfusion [37] with KissICP poses",{"name":466,"methodId":438,"linkable":132,"proposed":132,"self":69},"Ours with KissICP poses",{"name":468,"methodId":323,"linkable":132,"proposed":69,"self":69},"Puma [36] with own odometry",{"name":470,"methodId":438,"linkable":132,"proposed":132,"self":69},"Ours with own odometry",[472,474,476,478,480,482,484,486,488,490,492,494,496,498,500,502,504,506,508,510,512,514,516,518,520,522,524,526,528,530,532,534,536,538,540,542,544,546,548,550],[144,144,144,473,146,144,146,146,144],5.75,[144,148,144,475,146,144,146,146,144],38.45,[144,151,144,477,146,144,146,146,144],22.1,[144,154,144,479,146,144,146,146,144],67,[144,144,148,481,146,144,146,146,144],14.87,[144,148,148,483,146,144,146,146,144],20.02,[144,151,148,485,146,144,146,146,144],17.45,[144,157,148,487,146,144,146,146,144],68.85,[148,144,144,489,146,144,146,146,144],4.95,[148,148,144,491,146,144,146,146,144],46.79,[148,151,144,493,146,144,146,146,144],25.87,[148,154,144,495,146,144,146,146,144],68.15,[148,144,148,497,146,144,146,146,144],14.03,[148,148,148,499,146,144,146,146,144],25.46,[148,151,148,501,146,144,146,146,144],19.75,[148,157,148,503,146,144,146,146,144],69.5,[151,144,144,505,146,144,146,146,144],4.16,[151,148,144,507,146,144,146,146,144],37.2,[151,151,144,509,146,144,146,146,144],20.67,[151,154,144,511,146,144,146,146,144],73.31,[151,144,148,513,146,144,146,146,144],14.31,[151,148,148,515,146,144,146,146,144],24.39,[151,151,148,517,146,144,146,146,144],19.35,[151,157,148,519,146,144,146,146,144],68.7,[154,144,144,521,146,144,146,146,144],7.89,[154,148,144,523,146,144,146,146,144],9.14,[154,151,144,525,146,144,146,146,144],8.51,[154,154,144,527,146,144,146,146,144],68.04,[154,144,148,529,146,144,146,146,144],15.3,[154,148,148,531,146,144,146,146,144],71.91,[154,151,148,533,146,144,146,146,144],43.6,[154,157,148,535,146,144,146,146,144],57.27,[157,144,144,537,146,144,146,146,144],5.69,[157,148,144,539,146,144,146,146,144],11.23,[157,151,144,541,146,144,146,146,144],8.46,[157,154,144,543,146,144,146,146,144],77.26,[157,144,148,545,146,144,146,146,144],12.89,[157,148,148,547,146,144,146,146,144],22.21,[157,151,148,549,146,144,146,146,144],17.55,[157,157,148,551,146,144,146,146,144],74.37,[],[440],[],[],[557],"Simultaneous odometry and mapping; SHINE-Mapping and VDBFusion are fed KISS-ICP poses, NeRF-LOAM is shown with KISS-ICP poses and with its own odometry, Puma uses its own odometry; same voxel size for all; Newer College uses one of every five scans; accuracy, completion and Chamfer-L1 units not stated here (the same Newer College values are labelled cm in PIN-SLAM Table XI)",{"slug":559,"group":560,"sourceId":438,"sourceLabel":439,"table":561,"selfRows":441,"metrics":562,"seqs":568,"entrants":571,"cells":578,"outcomes":626,"locators":627,"hardware":628,"wordings":629,"notes":630},"nerfloam2023-table-2","nerfloam2023:Table 2","Table 2",[563,564,565,566],{"label":444,"unit":445,"statistic":107,"alignment":107},{"label":447,"unit":445,"statistic":107,"alignment":107},{"label":449,"unit":445,"statistic":107,"alignment":107},{"label":567,"unit":306,"statistic":107,"alignment":107},"F-score",[569,570],{"dataset":78,"sequence":107,"environment":456},{"dataset":83,"sequence":458,"environment":459},[572,574,576],{"name":573,"methodId":5,"linkable":132,"proposed":69,"self":132},"SHINE [50] with GT pose",{"name":575,"methodId":320,"linkable":132,"proposed":69,"self":69},"Vdbfusion [37] with GT pose",{"name":577,"methodId":438,"linkable":132,"proposed":132,"self":69},"Ours with GT pose",[579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,616,618,620,622,624],[144,144,144,580,146,144,146,146,144],4.17,[144,148,144,582,146,144,146,146,144],5.3,[144,151,144,584,146,144,146,146,144],4.74,[144,154,144,586,146,144,146,146,144],89.67,[144,144,148,588,146,144,146,146,144],8.32,[144,148,148,590,146,144,146,146,144],14.36,[144,151,148,592,146,144,146,146,144],11.34,[144,154,148,594,146,144,146,146,144],90.65,[148,144,144,596,146,144,146,146,144],4.12,[148,148,144,598,146,144,146,146,144],8.01,[148,151,144,600,146,144,146,146,144],6.07,[148,154,144,602,146,144,146,146,144],90.16,[148,144,148,604,146,144,146,146,144],6.87,[148,148,148,606,146,144,146,146,144],18.37,[148,151,148,608,146,144,146,146,144],12.61,[148,154,148,610,146,144,146,146,144],89.96,[151,144,144,612,146,144,146,146,144],3.15,[151,148,144,614,146,144,146,146,144],4.84,[151,151,144,157,146,144,146,146,144],[151,154,144,617,146,144,146,146,144],92.96,[151,144,148,619,146,144,146,146,144],6.86,[151,148,148,621,146,144,146,146,144],15.59,[151,151,148,623,146,144,146,146,144],11.24,[151,154,148,625,146,144,146,146,144],91.83,[],[561],[],[],[631],"Mapping quality with ground-truth poses for all methods (pure mapping ability); voxel size 20 cm; caption states F-score in % with a 10 cm threshold",[633,639,645],{"group":634,"slug":635,"sourceLabel":636,"table":637,"selfRows":441,"datasets":638},"pinslam2024:Table XI","pinslam2024-table-xi","Pan et al., 2024","Table XI",[83],{"group":640,"slug":641,"sourceLabel":6,"table":642,"selfRows":350,"datasets":643},"shinemapping2023:Table II","shinemapping2023-table-ii","Table II",[644],"MaiCity (synthetic)",{"group":646,"slug":647,"sourceLabel":6,"table":100,"selfRows":350,"datasets":648},"shinemapping2023:Table III","shinemapping2023-table-iii",[83],1790510660390]