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ELDOR

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Error code:   InfoError
Exception:    HfHubHTTPError
Message:      504 Server Error: Gateway Time-out for url: https://huggingface.co/api/datasets/IRSC/ELDOR/tree/28173c6e02cc66fc86b0911a5fd9b62cee62b291?recursive=True&expand=False
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 223, in compute_first_rows_from_streaming_response
                  info = get_dataset_config_info(path=dataset, config_name=config, token=hf_token)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 268, in get_dataset_config_info
                  builder = load_dataset_builder(
                            ^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1315, in load_dataset_builder
                  dataset_module = dataset_module_factory(
                                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1207, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1182, in dataset_module_factory
                  ).get_module()
                    ^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 638, in get_module
                  patterns = get_data_patterns(base_path, download_config=self.download_config)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/data_files.py", line 493, in get_data_patterns
                  return _get_data_files_patterns(resolver)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/data_files.py", line 290, in _get_data_files_patterns
                  data_files = pattern_resolver(pattern)
                               ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/data_files.py", line 372, in resolve_pattern
                  for filepath, info in fs.glob(fs_pattern, detail=True, **glob_kwargs).items():
                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_file_system.py", line 521, in glob
                  return super().glob(path, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/fsspec/spec.py", line 604, in glob
                  allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_file_system.py", line 563, in find
                  out = self._ls_tree(path, recursive=True, refresh=refresh, revision=resolved_path.revision, **kwargs)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_file_system.py", line 446, in _ls_tree
                  self._ls_tree(
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_file_system.py", line 463, in _ls_tree
                  for path_info in tree:
                                   ^^^^
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_api.py", line 3140, in list_repo_tree
                  for path_info in paginate(path=tree_url, headers=headers, params={"recursive": recursive, "expand": expand}):
                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/utils/_pagination.py", line 37, in paginate
                  hf_raise_for_status(r)
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 482, in hf_raise_for_status
                  raise _format(HfHubHTTPError, str(e), response) from e
              huggingface_hub.errors.HfHubHTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/api/datasets/IRSC/ELDOR/tree/28173c6e02cc66fc86b0911a5fd9b62cee62b291?recursive=True&expand=False

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ELDOR Data

  • Public dataset name: ELDOR
  • Paper: ELDOR: A Dataset and Benchmark for Illegal Gold Mining in the Amazon Rainforest
  • PDF: https://arxiv.org/pdf/2605.15397
  • Local dataset root: GoldMDD/data
  • Output folders: train/image, train/label, val/image, val/label, test/image, test/label (copied from GoldMining/Data/Orthomosaic and GoldMining/Data/Label).
  • Source folders: Drone_Orthomosaic_V1/Orthomosaic_org and Drone_Orthomosaic_V1/Just_labels
  • Matching rule: label to orthomosaic by filename mapping, then crop/reproject label to orthomosaic grid.
  • Canonical labels: 14 semantic classes (1..14) plus 0=Background after class merging in ELDOR.
  • Split rule in this dataset: train/val/test assignment is listed in the Split column of the spatial metadata table.

Citation 

@article{cui2026eldor,
  title={ELDOR: A Dataset and Benchmark for Illegal Gold Mining in the Amazon Rainforest},
  author={Cui, Kangning and Bohara, Surendra and Prasai, Suraj and Shao, Zishan and Tang, Wei and Pillaca, Martin and Flores, Edwin and Yang, Zhen and Larsen, Gregory and Dethier, Evan and Lutz, David and Morel, Jean-Michel and Silman, Miles and Pauca, Victor and Yang, Fan},
  journal={arXiv preprint arXiv:2605.15397},
  year={2026}
}

Spatial metadata table (source files + aligned output)

Site Ortho file Label source file CRS Ortho size (W x H px) Label size (W x H px) Ortho res (m/px x,y) Label res (m/px x,y) Ortho lon range Ortho lat range Label lon range Label lat range Output size (W x H px) Foreground area (ha) Acquisition date Split
AcumulacionAaron2B AcumulacionAaron2B_corrected.tif AcumulacionAaron2B_classified_corrected.tif EPSG:32719 38122x17643 38111x17760 0.056308, 0.056308 0.056323, 0.055936 [-70.419597, -70.399788] [-12.627643, -12.618558] [-70.419597, -70.399788] [-12.627643, -12.618558] 38122x17643 212.0501 2022-04-24 train
Anel Anel_corrected.tif Anel_classified_corrected.tif EPSG:32719 26909x24725 17662x17661 0.056620, 0.056620 0.058580, 0.059049 [-69.714044, -69.699976] [-12.713767, -12.701071] [-69.711529, -69.701974] [-12.711964, -12.702509] 18274x18420 102.7175 2022-04-08 test
Clavelito Clavelito_corrected.tif Clavelito_classified_corrected.tif EPSG:32719 19865x40307 19357x39684 0.075601, 0.075601 0.076376, 0.076781 [-70.605945, -70.591929] [-12.961391, -12.933761] [-70.605944, -70.592143] [-12.961387, -12.933761] 19556x40304 344.5330 2022-05-09 val
ElEngano ElEngano_corrected.tif ElEngano_classified_corrected.tif EPSG:32719 23616x24742 17473x17473 0.057233, 0.057233 0.057566, 0.058201 [-69.962478, -69.949966] [-13.020161, -13.007312] [-69.960769, -69.951459] [-13.018407, -13.009178] 17575x17770 101.2404 2022-03-03 test
Kotsimba Kotsimba_corrected.tif Kotsimba_classified_corrected.tif EPSG:32719 57331x53961 57227x53950 0.072760, 0.072760 0.072889, 0.072771 [-70.283655, -70.244998] [-13.136635, -13.100951] [-70.283654, -70.244999] [-13.136634, -13.100952] 57328x53959 693.7270 2022-05-26 train
Linda Linda2_corrected.tif Linda_classified_corrected.tif EPSG:32719 23601x23698 17352x17352 0.057632, 0.057632 0.059290, 0.059408 [-69.953378, -69.940789] [-13.020082, -13.007688] [-69.951874, -69.942353] [-13.018530, -13.009175] 17852x17888 98.8187 2022-02-28 test
Los5Rebeldes Los5Rebeldes_corrected.tif Los5Rebeldes_classified_corrected.tif EPSG:32719 43867x76279 33334x66668 0.030000, 0.030000 0.030140, 0.030001 [-70.672354, -70.660084] [-13.034336, -13.013573] [-70.670932, -70.661549] [-13.032969, -13.014831] 33490x66672 199.9257 2022-02-04 train
Nayda Nayda_corrected.tif Nayda_classified_corrected.tif EPSG:32719 22677x23480 17665x17665 0.056610, 0.056610 0.057532, 0.057385 [-69.721603, -69.709746] [-12.713363, -12.701313] [-69.720152, -69.710766] [-12.711798, -12.702607] 17954x17908 101.9933 2022-08-04 val
Paolita Paolita1_corrected.tif Paolita1_classified_corrected.tif EPSG:32719 58663x33523 54108x27055 0.055446, 0.055446 0.055275, 0.055441 [-69.630457, -69.600462] [-12.689889, -12.673012] [-69.629627, -69.602050] [-12.688983, -12.675355] 53943x27054 445.5226 2022-02-21 test
PlayaMirador1 PlayaMirador_corrected.tif PlayaMirador_classified_corrected.tif EPSG:32719 22911x22394 17608x17608 0.056792, 0.056792 0.056840, 0.057298 [-70.377536, -70.365474] [-13.063390, -13.051829] [-70.376072, -70.366793] [-13.062030, -13.057446] 17624x8883 50.1027 2022-03-10 train
PlayaMirador2 PlayaMirador_corrected.tif PlayaMirador_classified_corrected.tif EPSG:32719 22911x22394 17608x17608 0.056792, 0.056792 0.056840, 0.057298 [-70.377536, -70.365474] [-13.063390, -13.051829] [-70.376072, -70.366793] [-13.057446, -13.052861] 17624x8883 50.1021 2022-03-10 val
SantaInesDosMil SantaInesDosMil_corrected.tif SantaInesDosMil_classified_corrected.tif EPSG:32719 22883x24295 17185x17184 0.058193, 0.058193 0.058873, 0.058471 [-70.386657, -70.374307] [-13.063436, -13.050589] [-70.385274, -70.375895] [-13.061789, -13.052656] 17387x17267 100.9625 2022-03-10 test

Unified class mapping (ELDOR merged classes)

Canonical ID Class Merged from original IDs Color swatch Color (HEX) Area (ha) Percentage (%) Pixel count Alias names seen in metadata
1 Building 1,4 #8A6A3D 2.3745 0.09 6,367,488 Area urbana; Campamento minero
2 Mining raft 2 #7BEBFB 0.1467 0.01 461,925 Balsa; Balsa de mineria; Raft
3 Primary Forest 3 #B04C18 961.8239 38.45 3,308,805,552 Bosque primario; Primary forest
4 Heavy machinery 5,8,9,19 #EE92C6 0.0161 0.00 43,313 Cargador frontal; Excavadora; Maquinaria pesada; Volquete; Dump truck
5 Water bodies 6 #4F6F6F 321.2655 12.84 1,085,789,506 Cuerpos de agua
6 Agricultural crop 7 #84D08C 33.5185 1.34 105,688,237 Cultivos agricolas; Cultivo agricola; Cultivos; Crops
7 Compact mounds 10 #23F3E3 66.2310 2.65 215,787,431 Monticulo compacto; Compact mound
8 Gravel mounds 11 #585400 13.2680 0.53 42,049,847 Monticulos de cascajo
9 Grass 12 #8DB51D 28.3472 1.13 87,888,718 Pasto
10 Type 1 natural regeneration 13 #C2163A 228.5563 9.14 660,993,175 Regeneracion natural tipo 1; Regeneracion tipo 1; Type 1 disturbed vegetation
11 Type 2 natural regeneration 14 #F77757 523.1002 20.91 1,710,062,485 Regeneracion natural tipo 2; Regeneracion tipo 2; Type 2 disturbed vegetation
12 Bare ground 15 #2CD874 322.9909 12.91 882,535,708 Suelo desnudo; Bare soil
13 Sluice 16 #613991 0.0403 0.00 126,368 Tolva; Tolvas; Hopper
14 Vehicles 17,18 #969AAE 0.0092 0.00 85,244 Vehiculos; Vehiculos pequenos; Vehiculos pequeños; Small Vehicles

Label color plan (6-digit HEX)

ID Class HEX
0 Background #000000
1 Building #8A6A3D
2 Mining raft #7BEBFB
3 Primary Forest #B04C18
4 Heavy machinery #EE92C6
5 Water bodies #4F6F6F
6 Agricultural crop #84D08C
7 Compact mounds #23F3E3
8 Gravel mounds #585400
9 Grass #8DB51D
10 Type 1 natural regeneration #C2163A
11 Type 2 natural regeneration #F77757
12 Bare ground #2CD874
13 Sluice #613991
14 Vehicles #969AAE

Per-site classes using ELDOR merged mapping

Site Split Output ortho PNG Output label PNG Output size (W x H px) Output total pixels Merged class IDs present Merged class names present
AcumulacionAaron2B train AcumulacionAaron2B.png AcumulacionAaron2B.png 38122x17643 672,586,446 1,2,3,4,5,6,8,10,11,12,13,14 Building; Mining raft; Primary Forest; Heavy machinery; Water bodies; Agricultural crop; Gravel mounds; Type 1 natural regeneration; Type 2 natural regeneration; Bare ground; Sluice; Vehicles
Kotsimba train Kotsimba.png Kotsimba.png 57328x53959 3,093,361,552 1,3,5,6,7,10,11,12 Building; Primary Forest; Water bodies; Agricultural crop; Compact mounds; Type 1 natural regeneration; Type 2 natural regeneration; Bare ground
Los5Rebeldes train Los5Rebeldes.png Los5Rebeldes.png 33490x66672 2,232,845,280 1,3,5,6,7,10,11,12,14 Building; Primary Forest; Water bodies; Agricultural crop; Compact mounds; Type 1 natural regeneration; Type 2 natural regeneration; Bare ground; Vehicles
PlayaMirador1 train PlayaMirador1.png PlayaMirador1.png 17624x8883 156,553,992 1,3,5,6,9,10,11,12 Building; Primary Forest; Water bodies; Agricultural crop; Grass; Type 1 natural regeneration; Type 2 natural regeneration; Bare ground
Clavelito val Clavelito.png Clavelito.png 19556x40304 788,185,024 1,3,4,5,6,7,10,11,12 Building; Primary Forest; Heavy machinery; Water bodies; Agricultural crop; Compact mounds; Type 1 natural regeneration; Type 2 natural regeneration; Bare ground
Nayda val Nayda.png Nayda.png 17954x17908 321,520,232 1,2,3,5,6,8,10,11,12,13 Building; Mining raft; Primary Forest; Water bodies; Agricultural crop; Gravel mounds; Type 1 natural regeneration; Type 2 natural regeneration; Bare ground; Sluice
PlayaMirador2 val PlayaMirador2.png PlayaMirador2.png 17624x8883 156,553,992 1,3,5,6,9,10,11,12 Building; Primary Forest; Water bodies; Agricultural crop; Grass; Type 1 natural regeneration; Type 2 natural regeneration; Bare ground
Anel test Anel.png Anel.png 18274x18420 336,607,080 1,2,3,5,6,8,10,11,12,13 Building; Mining raft; Primary Forest; Water bodies; Agricultural crop; Gravel mounds; Type 1 natural regeneration; Type 2 natural regeneration; Bare ground; Sluice
ElEngano test ElEngano.png ElEngano.png 17575x17770 312,307,750 3,5,6,10,11,12 Primary Forest; Water bodies; Agricultural crop; Type 1 natural regeneration; Type 2 natural regeneration; Bare ground
Linda test Linda.png Linda.png 17852x17888 319,336,576 1,2,3,5,10,11,12,13 Building; Mining raft; Primary Forest; Water bodies; Type 1 natural regeneration; Type 2 natural regeneration; Bare ground; Sluice
Paolita test Paolita.png Paolita.png 53943x27054 1,459,373,922 1,2,3,5,6,8,10,11,12,13 Building; Mining raft; Primary Forest; Water bodies; Agricultural crop; Gravel mounds; Type 1 natural regeneration; Type 2 natural regeneration; Bare ground; Sluice
SantaInesDosMil test SantaInesDosMil.png SantaInesDosMil.png 17387x17267 300,221,329 1,3,5,6,10,11,12 Building; Primary Forest; Water bodies; Agricultural crop; Type 1 natural regeneration; Type 2 natural regeneration; Bare ground

Generation workflow

  • Source orthomosaic/label alignment and original canonical labels come from GoldMining/Data.
  • ELDOR labels are remapped from the original 19-class canonical IDs into a 14-class merged scheme (stored only under GoldMDD/data/*/label).
  • Merge rules:
    • Heavy machinery = original IDs 5 (Front loader), 8 (Excavator), 9 (Heavy machinery), 19 (Dump truck)
    • Vehicles = original IDs 17 (Vehicles), 18 (Small vehicles)
    • Building = original IDs 1 (Urban area), 4 (Mining camp)
  • Heatmap output: GoldMDD/site_class_pixel_counts_heatmap_merged.png
  • Heatmap CSV: GoldMDD/site_class_pixel_counts_merged.csv
  • Train/val/test distribution plot: GoldMDD/train_val_test_class_distribution_merged.png
  • Train/val/test distribution CSV: GoldMDD/train_val_test_class_distribution_merged.csv

Cropped patch dataset (data-cropped)

  • Output root: GoldMDD/data-cropped
  • Patch size: 512x512
  • Stride: 256
  • Filtering rule: drop a patch if background pixels in the merged label (label==0) are >80%.
  • Windowing rule: full windows only (no padding).
  • Patch naming: matching basenames per pair, e.g. image AcumulacionAaron2B_2_3.jpg and label AcumulacionAaron2B_2_3.png (1-based row/col indices).
  • Storage format: image patches = JPEG, label patches = PNG (lossless class IDs).
  • Folder structure: train/image, train/label, val/image, val/label, test/image, test/label.

Crop summary by split

Split # Sites Candidate windows Kept patches Dropped (>80% bg) Kept ratio
train 4 91,869 65,798 26,071 0.716
val 3 18,603 15,988 2,615 0.859
test 5 40,172 40,095 77 0.998
Total 12 150,644 121,881 28,763 0.809

Crop summary by site

Split Site Source size (W x H) Candidate windows Kept patches Dropped (>80% bg) Kept ratio
train AcumulacionAaron2B 38122x17643 9,849 9,849 0 1.000
train Kotsimba 57328x53959 46,398 20,327 26,071 0.438
train Los5Rebeldes 33490x66672 33,411 33,411 0 1.000
train PlayaMirador1 17624x8883 2,211 2,211 0 1.000
val Clavelito 19556x40304 11,700 9,085 2,615 0.776
val Nayda 17954x17908 4,692 4,692 0 1.000
val PlayaMirador2 17624x8883 2,211 2,211 0 1.000
test Anel 18274x18420 4,900 4,890 10 0.998
test ElEngano 17575x17770 4,556 4,556 0 1.000
test Linda 17852x17888 4,624 4,577 47 0.990
test Paolita 53943x27054 21,736 21,716 20 0.999
test SantaInesDosMil 17387x17267 4,356 4,356 0 1.000
  • Summary CSVs:
    • GoldMDD/data-cropped/crop_summary_by_split.csv
    • GoldMDD/data-cropped/crop_summary_by_site.csv
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Paper for IRSC/ELDOR