crs-l2-svalbard-polar-areas
Map (reference ← swipe → agent)
0:00
svalbard_glaciers — I need an honest size ranking of these high-latitude glaciers using true geographic area, not the distorted values you'd get from raw lat/lon coordinates. Pick the most appropriate coordinate system for measuring area at these latitudes and give me the top 20 by area as CSV: columns name, area_km2, bbox_minx_polar, bbox_miny_polar, bbox_maxx_polar, bbox_maxy_polar, crs_epsg — bboxes in the same projected metres as the area, and crs_epsg the integer EPSG code of the CRS you used. Sort by area_km2 descending. File: svalbard_glaciers_top20.csv.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:03
Let me start by understanding the task. I need to work with Svalbard glaciers data, calculate their true geographic area using an appropriate coordinate system (likely a polar stereographic projection), and produce a CSV with the top 20 largest glaciers. Let me first explore what data is available.
0:03
Bash
ls /work/ 2>/dev/null; find /work -name "*.gpkg" -o -name "*.shp" -o -name "*.ge...
svalbard_glaciers_wgs84.gpkg /work/svalbard_glaciers_wgs84.gpkg
0:07
Let me first explore the data to understand its structure.
0:07
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('svalbard_glaciers_wgs84.gpkg')
print('Columns:', gdf.columns.tolist())
print('CRS:', gdf.crs)
print('Shape:', gdf.shape)
print(gdf.head(3).to_string())
"
Columns: ['id', 'name', 'subtype', 'class', 'geometry']
CRS: EPSG:4326
Shape: (169, 5)
id name subtype class geometry
0 af2fac62-8f17-35dd-bb4b-cd7ac5f906b0 Ahlmannfonna glacier glacier MULTIPOLYGON (((22.78865 80.14451, 22.73235 80.14495, 22.69574 80.14821, 22.66462 80.15106, 22.6329 80.14451, 22.57765 80.13976, 22.55505 80.13414, 22.51142 80.13583, 22.48278 80.13205, 22.49623 80.13004, 22.53681 80.12985, 22.54964 80.12486, 22.52446 80.12342, 22.47185 80.11975, 22.45328 80.11603, 22.46797 80.11128, 22.51746 80.1109, 22.54743 80.10666, 22.57741 80.10356, 22.58093 80.09622, 22.61695 80.08699, 22.67021 80.08735, 22.73254 80.08977, 22.78001 80.09751, 22.79382 80.10561, 22.82289 80.10564, 22.86503 80.10751, 22.87788 80.11534, 22.87399 80.11986, 22.8692 80.12325, 22.84801 80.12588, 22.84242 80.13143, 22.83658 80.13456, 22.83421 80.14103, 22.78865 80.14451)))
1 12ba70b6-6f49-35fa-a072-bfdc8321c987 Aldegondabreen glacier glacier MULTIPOLYGON (((14.02517 77.97816, 14.02557 77.97799, 14.02727 77.97781, 14.02869 77.97764, 14.02881 77.97744, 14.02747 77.97735, 14.02324 77.97788, 14.02064 77.9774, 14.01975 77.97719, 14.01766 77.97713, 14.0179 77.97676, 14.01695 77.97644, 14.01414 77.97621, 14.0133 77.97553, 14.01017 77.97448, 14.0108 77.97419, 14.01184 77.97372, 14.01547 77.97285, 14.01385 77.97273, 14.01403 77.97205, 14.01701 77.97166, 14.01743 77.97102, 14.01741 77.97047, 14.0162 77.9699, 14.01361 77.96982, 14.01065 77.96995, 14.00724 77.96954, 14.00687 77.96908, 14.00547 77.96857, 14.00426 77.96763, 14.00739 77.96724, 14.01135 77.9674, 14.01586 77.96664, 14.01831 77.96647, 14.0219 77.96663, 14.0242 77.9665, 14.02706 77.96621, 14.02822 77.96627, 14.02898 77.96616, 14.03301 77.96643, 14.03516 77.96656, 14.03586 77.96607, 14.03685 77.96538, 14.03635 77.9645, 14.03334 77.96398, 14.03156 77.96384, 14.02983 77.96387, 14.02592 77.96449, 14.0241 77.96471, 14.02202 77.96465, 14.02091 77.96449, 14.01533 77.9645, 14.0117 77.96437, 14.00838 77.96429, 14.00494 77.9636, 13.99986 77.96357, 13.99931 77.96306, 14.00073 77.96312, 14.00093 77.96254, 14.00349 77.96271, 14.00184 77.96214, 14.00059 77.96211, 14.00026 77.96183, 14.00164 77.96147, 14.00257 77.96111, 14.00508 77.96148, 14.00717 77.96135, 14.00981 77.96145, 14.01208 77.96194, 14.0153 77.96156, 14.01758 77.96165, 14.02114 77.96168, 14.02123 77.96093, 14.02404 77.96085, 14.03111 77.96036, 14.03445 77.95963, 14.04166 77.95957, 14.04882 77.95995, 14.05006 77.95946, 14.05686 77.9599, 14.06337 77.95881, 14.065 77.95848, 14.06667 77.95904, 14.06916 77.95943, 14.07174 77.9597, 14.07221 77.95954, 14.07234 77.95898, 14.07359 77.95899, 14.07479 77.95912, 14.07675 77.95979, 14.07556 77.96016, 14.0754 77.96055, 14.07797 77.96146, 14.07944 77.96174, 14.08097 77.96152, 14.08365 77.96153, 14.08549 77.96119, 14.08737 77.96148, 14.08908 77.96218, 14.09241 77.96408, 14.09347 77.9642, 14.09313 77.96469, 14.09493 77.96441, 14.09637 77.96462, 14.10013 77.96523, 14.09708 77.96595, 14.0957 77.96614, 14.09385 77.96576, 14.09159 77.96507, 14.09118 77.96535, 14.09343 77.96622, 14.09403 77.96686, 14.09435 77.96725, 14.09663 77.96772, 14.09923 77.96797, 14.10179 77.96818, 14.10484 77.96841, 14.10706 77.96864, 14.10867 77.96836, 14.11125 77.96795, 14.11267 77.96774, 14.11527 77.96673, 14.11906 77.96659, 14.12295 77.96652, 14.12449 77.96704, 14.12286 77.96773, 14.12088 77.96848, 14.11887 77.96938, 14.11812 77.96992, 14.11799 77.97044, 14.11919 77.97099, 14.12127 77.97125, 14.12404 77.97132, 14.12629 77.97148, 14.1293 77.9717, 14.13194 77.97196, 14.13407 77.97222, 14.13738 77.97245, 14.13874 77.97286, 14.14097 77.9728, 14.1414 77.97243, 14.14265 77.97206, 14.14572 77.97245, 14.146 77.97285, 14.14704 77.97294, 14.15036 77.97254, 14.15162 77.97291, 14.14822 77.97417, 14.14715 77.97457, 14.13727 77.97386, 14.13422 77.9737, 14.13107 77.97379, 14.12832 77.97421, 14.12558 77.97462, 14.12314 77.97506, 14.12009 77.97546, 14.11777 77.97568, 14.114 77.97568, 14.11125 77.97646, 14.10875 77.97712, 14.10733 77.9773, 14.10627 77.97743, 14.10393 77.97772, 14.10311 77.97787, 14.10243 77.978, 14.10047 77.97795, 14.10036 77.97795, 14.09906 77.97824, 14.09807 77.97829, 14.09766 77.97799, 14.09757 77.97801, 14.0954 77.97849, 14.09523 77.97853, 14.09295 77.97764, 14.09069 77.97726, 14.08664 77.97733, 14.08325 77.97743, 14.08069 77.97736, 14.07866 77.97796, 14.08202 77.97915, 14.07977 77.97964, 14.08411 77.98004, 14.0846 77.98029, 14.08624 77.9811, 14.08945 77.98181, 14.08862 77.98211, 14.0865 77.9825, 14.0845 77.98206, 14.08177 77.98181, 14.07918 77.98144, 14.07685 77.98134, 14.07535 77.98135, 14.06978 77.98104, 14.06286 77.98096, 14.05852 77.98079, 14.05587 77.98079, 14.04893 77.98059, 14.04009 77.98003, 14.03661 77.97942, 14.03073 77.97871, 14.02517 77.97816)))
2 5919887f-cdb2-3283-a884-b1146bdda8ee Altbreen glacier glacier MULTIPOLYGON (((16.06338 78.22499, 16.06381 78.22427, 16.0663 78.22378, 16.0712 78.22364, 16.07815 78.22383, 16.0815 78.22451, 16.08132 78.22528, 16.08122 78.22562, 16.08081 78.22705, 16.08416 78.22865, 16.0924 78.22919, 16.09849 78.22826, 16.10054 78.228, 16.10862 78.22695, 16.11216 78.22579, 16.11583 78.22458, 16.12458 78.22194, 16.12681 78.22173, 16.1293 78.22238, 16.13042 78.22364, 16.1287 78.22628, 16.12409 78.22823, 16.12372 78.22838, 16.119 78.23083, 16.12123 78.23178, 16.12956 78.23365, 16.13612 78.23432, 16.14153 78.23417, 16.14762 78.23334, 16.15204 78.23464, 16.15273 78.23607, 16.15153 78.23719, 16.14621 78.2382, 16.13514 78.23848, 16.12287 78.23789, 16.11188 78.23679, 16.10879 78.23561, 16.10235 78.2322, 16.0972 78.23157, 16.08167 78.23078, 16.0712 78.22872, 16.06313 78.22612, 16.06338 78.22499)))
0:18
The data is in EPSG:4326 (WGS84 lat/lon). For accurate area measurement at high latitudes (Svalbard is around 74-81°N), I need to use a suitable projected CRS. For Svalbard, the best options are: - EPSG:32631-32633 (UTM zones 31N to 33N) - but Svalbard spans multiple UTM zones - EPSG:3413 (NSIDC Sea Ice Polar Stereographic North) - good for polar regions - EPSG:3995 (WGS 84 / Arctic Polar Stereographic) - specifically designed for Arctic - EPSG:5936 (WGS 84 / EPSG Arctic zone 1-17) - EPSG Arctic zones The most appropriate for Svalbard would be either EPSG:3413 (NSIDC Sea Ice Polar Stereographic North) or EPSG:3995 (Arctic Polar Stereographic). Let me think... For Svalbard (74°N to 81°N), UTM zones 31-33 are options but the region spans multiple zones. A polar stereographic projection is more appropriate for consistent area measurement across the whole archipelago. EPSG:3413 - NSIDC Sea Ice Polar Stereographic North - standard for Arctic, centered at 0°E, latitude of origin 70°N EPSG:3995 - WGS 84 / Arctic Polar Stereographic - also good, latitude of origin 71°N Both are good for Svalbard. Let me use EPSG:3413 which is commonly used for Arctic area measurements, or EPSG:6931 (WGS 84 / NSIDC Sea Ice Polar Stereographic North). Actually let me check what's available. Let me compute areas using a polar stereographic projection. I'll use EPSG:3413 (NSIDC Sea Ice Polar Stereographic North) which has: - Central Meridian: 0° - Latitude of Origin: 70°N - Standard Parallel: 70°N Actually, let me also consider EPSG:3995 (WGS 84 / Arctic Polar Stereographic) which is another common choice. I'll go with EPSG:3413 as it's widely used for Arctic area calculations. Actually, let me check what pyproj supports. Let me just write the script.
0:18
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('svalbard_glaciers_wgs84.gpkg')
# Check the CRS that are available
from pyproj import CRS
# EPSG:3413 - NSIDC Sea Ice Polar Stereographic North
crs_3413 = CRS.from_epsg(3413)
print('EPSG:3413:', crs_3413)
print()
# EPSG:3995 - WGS 84 / Arctic Polar Stereographic
crs_3995 = CRS.from_epsg(3995)
print('EPSG:3995:', crs_3995)
print()
# Let's see the latitude range
print('Lat range:', gdf.geometry.bounds.miny.min(), gdf.geometry.bounds.maxy.max())
print('Lon range:', gdf.geometry.bounds.minx.min(), gdf.geometry.bounds.maxx.max())
"
EPSG:3413: EPSG:3413 EPSG:3995: EPSG:3995 Lat range: 77.4630249 80.3115547 Lon range: 10.7877123 33.4866675
0:25
Svalbard is located between roughly 77°N and 81°N. For measuring area at high latitudes, the most appropriate projected coordinate system would be a polar stereographic projection. Two common ones: 1. **EPSG:3413** - NSIDC Sea Ice Polar Stereographic North (true scale at 70°N) 2. **EPSG:3995** - WGS 84 / Arctic Polar Stereographic (true scale at 71°N) 3. **EPSG:5938** - WGS 84 / EPSG Arctic / zone 2 (but this is a UTM zone) 4. **EPSG:32633** etc - UTM zones (33N etc) For Svalbard, the most appropriate is EPSG:3413 or EPSG:3995. Both are polar stereographic projections designed for the Arctic. Let me use EPSG:3413 (NSIDC Sea Ice Polar Stereographic North) which is commonly used for Arctic area calculations, or EPSG:3995 (Arctic Polar Stereographic). Actually, let me think about what's best for area measurement specifically in Svalbard. Both EPSG:3413 and EPSG:3995 are polar stereographic projections. EPSG:3413 uses the International 1924 ellipsoid and has latitude of true scale at 70°N. EPSG:3995 uses WGS 84 and has latitude of origin at 71°N. Both should work well for Svalbard. Let me use EPSG:3413 as it's the standard NSIDC polar stereographic projection commonly used for Arctic area measurements. Actually, let me double-check what's most appropriate. For Svalbard (77-81°N), a polar stereographic projection centered on the North Pole is ideal. Both EPSG:3413 and EPSG:3995 work well. Let me go with EPSG:3413.
0:25
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('svalbard_glaciers_wgs84.gpkg')
# Project to EPSG:3413 (NSIDC Sea Ice Polar Stereographic North) - appropriate for high Arctic
gdf_polar = gdf.to_crs(epsg=3413)
# Calculate area in square km
gdf_polar['area_km2'] = gdf_polar.geometry.area / 1e6
# Get bounding boxes in projected coordinates
bounds = gdf_polar.geometry.bounds
gdf_polar['bbox_minx_polar'] = bounds.minx
gdf_polar['bbox_miny_polar'] = bounds.miny
gdf_polar['bbox_maxx_polar'] = bounds.maxx
gdf_polar['bbox_maxy_polar'] = bounds.maxy
# Create output dataframe
result = gdf_polar[['name', 'area_km2', 'bbox_minx_polar', 'bbox_miny_polar', 'bbox_maxx_polar', 'bbox_maxy_polar']].copy()
result['crs_epsg'] = 3413
# Sort by area descending and take top 20
result = result.sort_values('area_km2', ascending=False).head(20)
print(result.to_string())
print()
print('Sum of area:', result['area_km2'].sum())
"
name area_km2 bbox_minx_polar bbox_miny_polar bbox_maxx_polar bbox_maxy_polar crs_epsg 140 Sørfonna 4146.995028 1.018601e+06 -472800.391267 1.093407e+06 -365990.290930 3413 156 Vestfonna 2272.183117 9.559240e+05 -493366.304647 1.014590e+06 -420978.166085 3413 168 Åsgardfonna 872.249186 9.706723e+05 -558393.739427 1.024973e+06 -525034.100104 3413 78 Kvitøyjøkulen 595.277158 1.028270e+06 -251073.056927 1.052280e+06 -211860.173706 3413 149 Valhallfonna 405.018065 9.715117e+05 -527453.984647 1.003139e+06 -505582.963556 3413 103 Nordmannsfonna 125.595033 1.128862e+06 -577716.841074 1.149204e+06 -563508.015625 3413 132 Sveabreen 121.142714 1.042379e+06 -645032.461588 1.070491e+06 -634900.873833 3413 44 Gruvfonna 109.265816 1.144709e+06 -629651.817666 1.156022e+06 -608114.427637 3413 160 Veteranen 92.382704 1.023748e+06 -552570.066540 1.045630e+06 -532772.411813 3413 54 Himingbjørgfonna 80.514240 1.019942e+06 -558249.721488 1.032767e+06 -542067.459536 3413 52 Hellefonna 78.939804 1.137721e+06 -599557.161679 1.153449e+06 -586377.072944 3413 53 Heuglinbreen 68.226242 1.124554e+06 -561227.661019 1.137551e+06 -552367.730797 3413 29 Eidembreen 64.223783 1.067451e+06 -670591.530625 1.073502e+06 -653568.174195 3413 102 Nordenskiöldbreen 64.191280 1.082148e+06 -580288.628158 1.091822e+06 -569000.222702 3413 60 Isrosa 45.113467 1.145942e+06 -580366.488992 1.153648e+06 -567251.221209 3413 27 Edvardbreen 44.473139 1.164649e+06 -614218.850293 1.173314e+06 -600447.124009 3413 32 Esmarkbreen 41.160289 1.075609e+06 -661298.862900 1.088685e+06 -653342.465556 3413 154 Venernbreen 40.835318 1.074999e+06 -669967.183774 1.081719e+06 -657177.431725 3413 25 Dunérbreen 38.700749 9.708777e+05 -526415.352256 9.844084e+05 -519393.154511 3413 37 Fridtjovbreen 37.598264 1.131454e+06 -675329.642301 1.145279e+06 -669881.638282 3413 Sum of area: 9344.085398720936
0:30
The results look reasonable. Let me now write the CSV file with the proper formatting.
0:30
Bash
cd /work && python3 << 'PYEOF'
cd /work && python3 << 'PYEOF'
import geopandas as gpd
import pandas as pd
gdf = gpd.read_file('svalbard_glaciers_wgs84.gpkg')
# EPSG:3413 - NSIDC Sea Ice Polar Stereographic North - appropriate for Svalbard (77-81°N)
# This projection preserves area well at high latitudes
gdf_polar = gdf.to_crs(epsg=3413)
# Calculate area in square km
gdf_polar['area_km2'] = gdf_polar.geometry.area / 1e6
# Get bounding boxes in projected coordinates
bounds = gdf_polar.geometry.bounds
gdf_polar['bbox_minx_polar'] = bounds.minx
gdf_polar['bbox_miny_polar'] = bounds.miny
gdf_polar['bbox_maxx_polar'] = bounds.maxx
gdf_polar['bbox_maxy_polar'] = bounds.maxy
# Create output dataframe
result = gdf_polar[['name', 'area_km2', 'bbox_minx_polar', 'bbox_miny_polar', 'bbox_maxx_polar', 'bbox_maxy_polar']].copy()
result['crs_epsg'] = 3413
# Sort by area descending and take top 20
result = result.sort_values('area_km2', ascending=False).head(20)
# Round area_km2 to reasonable precision
result['area_km2'] = result['area_km2'].round(6)
# Write CSV
result.to_csv('/work/svalbard_glaciers_top20.csv', index=False)
print("Done. CSV written.")
print(result.to_string())
PYEOF
Done. CSV written.
name area_km2 bbox_minx_polar bbox_miny_polar bbox_maxx_polar bbox_maxy_polar crs_epsg
140 Sørfonna 4146.995028 1.018601e+06 -472800.391267 1.093407e+06 -365990.290930 3413
156 Vestfonna 2272.183117 9.559240e+05 -493366.304647 1.014590e+06 -420978.166085 3413
168 Åsgardfonna 872.249186 9.706723e+05 -558393.739427 1.024973e+06 -525034.100104 3413
78 Kvitøyjøkulen 595.277158 1.028270e+06 -251073.056927 1.052280e+06 -211860.173706 3413
149 Valhallfonna 405.018065 9.715117e+05 -527453.984647 1.003139e+06 -505582.963556 3413
103 Nordmannsfonna 125.595033 1.128862e+06 -577716.841074 1.149204e+06 -563508.015625 3413
132 Sveabreen 121.142714 1.042379e+06 -645032.461588 1.070491e+06 -634900.873833 3413
44 Gruvfonna 109.265816 1.144709e+06 -629651.817666 1.156022e+06 -608114.427637 3413
160 Veteranen 92.382704 1.023748e+06 -552570.066540 1.045630e+06 -532772.411813 3413
54 Himingbjørgfonna 80.514240 1.019942e+06 -558249.721488 1.032767e+06 -542067.459536 3413
52 Hellefonna 78.939804 1.137721e+06 -599557.161679 1.153449e+06 -586377.072944 3413
53 Heuglinbreen 68.226242 1.124554e+06 -561227.661019 1.137551e+06 -552367.730797 3413
29 Eidembreen 64.223783 1.067451e+06 -670591.530625 1.073502e+06 -653568.174195 3413
102 Nordenskiöldbreen 64.191280 1.082148e+06 -580288.628158 1.091822e+06 -569000.222702 3413
60 Isrosa 45.113467 1.145942e+06 -580366.488992 1.153648e+06 -567251.221209 3413
27 Edvardbreen 44.473139 1.164649e+06 -614218.850293 1.173314e+06 -600447.124009 3413
32 Esmarkbreen 41.160289 1.075609e+06 -661298.862900 1.088685e+06 -653342.465556 3413
154 Venernbreen 40.835318 1.074999e+06 -669967.183774 1.081719e+06 -657177.431725 3413
25 Dunérbreen 38.700749 9.708777e+05 -526415.352256 9.844084e+05 -519393.154511 3413
37 Fridtjovbreen 37.598264 1.131454e+06 -675329.642301 1.145279e+06 -669881.638282 3413
0:34
Let me verify the output file is correct and check the CSV content.
0:34
Read
/work/svalbard_glaciers_top20.csv
1 name,area_km2,bbox_minx_polar,bbox_miny_polar,bbox_maxx_polar,bbox_maxy_polar,crs_epsg
2 Sørfonna,4146.995028,1018600.817882212,-472800.3912665622,1093407.1674378568,-365990.2909299612,3413
3 Vestfonna,2272.183117,955923.9805055511,-493366.30464676005,1014589.5375176889,-420978.1660846758,3413
4 Åsgardfonna,872.249186,970672.3159938941,-558393.7394266115,1024973.2110542867,-525034.1001042881,3413
5 Kvitøyjøkulen,595.277158,1028269.5074980733,-251073.05692732212,1052280.1255731431,-211860.17370643766,3413
6 Valhallfonna,405.018065,971511.670097754,-527453.9846470426,1003139.1997554839,-505582.9635556986,3413
7 Nordmannsfonna,125.595033,1128862.4515745593,-577716.8410743554,1149204.2654806755,-563508.01562506,3413
8 Sveabreen,121.142714,1042378.8994917835,-645032.4615881244,1070491.2293700927,-634900.8738330957,3413
9 Gruvfonna,109.265816,1144708.8887145082,-629651.8176660625,1156022.2028977482,-608114.4276372788,3413
10 Veteranen,92.382704,1023748.0056298103,-552570.0665400377,1045629.5864769553,-532772.4118134979,3413
11 Himingbjørgfonna,80.51424,1019942.2563029992,-558249.7214880211,1032767.2559255561,-542067.4595360316,3413
12 Hellefonna,78.939804,1137721.2132937114,-599557.1616786135,1153448.587803471,-586377.0729442405,3413
13 Heuglinbreen,68.226242,1124554.481148079,-561227.6610193359,1137550.8359948718,-552367.730796976,3413
14 Eidembreen,64.223783,1067450.8900832098,-670591.5306250262,1073502.0562839245,-653568.1741950389,3413
15 Nordenskiöldbreen,64.19128,1082148.1499660225,-580288.628157692,1091821.8942982997,-569000.2227019768,3413
16 Isrosa,45.113467,1145941.681893076,-580366.4889922002,1153648.207848919,-567251.2212087518,3413
17 Edvardbreen,44.473139,1164648.5289905206,-614218.8502931708,1173314.2171653411,-600447.1240089986,3413
18 Esmarkbreen,41.160289,1075609.0346814487,-661298.8628999034,1088684.5918938657,-653342.4655558533,3413
19 Venernbreen,40.835318,1074998.6217814437,-669967.1837736613,1081719.4183093198,-657177.4317250035,3413
20 Dunérbreen,38.700749,970877.7430687764,-526415.3522561246,984408.4476399018,-519393.1545105753,3413
21 Fridtjovbreen,37.598264,1131454.2915404618,-675329.6423005203,1145279.019816095,-669881.6382822026,3413
0:38
Let me verify this file looks good. The task asks for columns: name, area_km2, bbox_minx_polar, bbox_miny_polar, bbox_maxx_polar, bbox_maxy_polar, crs_epsg. The values look correct - area in km^2, bboxes in projected metres, CRS is EPSG:3413 (NSIDC Sea Ice Polar Stereographic North). This is the most appropriate projection for area measurement in Svalbard (77°N to 81°N) because it's an equal-area projection designed for the Arctic. Let me double-check that the CSV has proper headers and is properly formatted.
0:38
0:38
Bash
cd /work && python3 -c "
cd /work && python3 -c "
# Quick verification
import pandas as pd
df = pd.read_csv('svalbard_glaciers_top20.csv')
print('Columns:', df.columns.tolist())
print('Row count:', len(df))
print('CRS EPSG:', df['crs_epsg'].unique())
print('dtypes:')
print(df.dtypes)
print()
print('Top 5:')
print(df.head(5).to_string())
"
Columns: ['name', 'area_km2', 'bbox_minx_polar', 'bbox_miny_polar', 'bbox_maxx_polar', 'bbox_maxy_polar', 'crs_epsg']
Row count: 20
CRS EPSG: [3413]
dtypes:
name str
area_km2 float64
bbox_minx_polar float64
bbox_miny_polar float64
bbox_maxx_polar float64
bbox_maxy_polar float64
crs_epsg int64
dtype: object
Top 5:
name area_km2 bbox_minx_polar bbox_miny_polar bbox_maxx_polar bbox_maxy_polar crs_epsg
0 Sørfonna 4146.995028 1.018601e+06 -472800.391267 1.093407e+06 -365990.290930 3413
1 Vestfonna 2272.183117 9.559240e+05 -493366.304647 1.014590e+06 -420978.166085 3413
2 Åsgardfonna 872.249186 9.706723e+05 -558393.739427 1.024973e+06 -525034.100104 3413
3 Kvitøyjøkulen 595.277158 1.028270e+06 -251073.056927 1.052280e+06 -211860.173706 3413
4 Valhallfonna 405.018065 9.715117e+05 -527453.984647 1.003139e+06 -505582.963556 3413
0:43
The file looks correct. Let me summarize what I did.
0:43