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"
}
0:05
Let me start by understanding the data. I need to look at the Svalbard glaciers dataset and determine the most appropriate coordinate system for measuring area at high latitudes (around 78-81°N). For polar regions, the best choice would be a polar stereographic projection. For Svalbard specifically, the most appropriate CRS would be EPSG:3411 (NSIDC Sea Ice Polar Stereographic North) or EPSG:32633 (UTM zone 33N) - but actually, for high latitudes, UTM zones are only valid up to 84°N. Let me think about this more carefully. Svalbard is located between approximately 74°N and 81°N. The most commonly used CRS for Svalbard area calculations would be: 1. EPSG:3411 - NSIDC Sea Ice Polar Stereographic North (true scale at 70°N) 2. EPSG:3995 - WGS 84 / Arctic Polar Stereographic (used for Arctic) 3. EPSG:32631 to 32636 - UTM zones 31-36 cover Svalbard areas, but Svalbard spans multiple zones Actually, for measuring area accurately in Svalbard, the best would be a polar stereographic projection. EPSG:3995 (Arctic Polar Stereographic) is commonly used, or EPSG:3411. Let me first explore the data.
0:05
Bash
ls /work/
svalbard_glaciers_wgs84.gpkg
0:05
Bash
find /work -name "*svalbard*" -o -name "*glacier*" 2>/dev/null | head -20
/work/svalbard_glaciers_wgs84.gpkg
0:07
Let's explore the data first.
0:07
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('svalbard_glaciers_wgs84.gpkg')
print(gdf.head())
print(gdf.columns.tolist())
print(gdf.crs)
print(len(gdf))
"
id ... geometry 0 af2fac62-8f17-35dd-bb4b-cd7ac5f906b0 ... MULTIPOLYGON (((22.78865 80.14451, 22.73235 80... 1 12ba70b6-6f49-35fa-a072-bfdc8321c987 ... MULTIPOLYGON (((14.02517 77.97816, 14.02557 77... 2 5919887f-cdb2-3283-a884-b1146bdda8ee ... MULTIPOLYGON (((16.06338 78.22499, 16.06381 78... 3 cdbc3ec1-ce56-3dbc-89fe-f4bce34bd5a8 ... MULTIPOLYGON (((12.92179 78.40649, 12.95164 78... 4 81e675dd-427e-3cd8-ac05-929fbbd3c30e ... MULTIPOLYGON (((11.88304 78.88352, 11.88399 78... [5 rows x 5 columns] ['id', 'name', 'subtype', 'class', 'geometry'] EPSG:4326 169
0:17
So we have 169 glaciers in Svalbard, in EPSG:4326 (WGS84). For measuring area at high latitudes (Svalbard is roughly 74°N to 81°N), the most appropriate projected coordinate system would be a Polar Stereographic projection. The best choice is EPSG:3413 — WGS 84 NSIDC Sea Ice Polar Stereographic North, which is designed for the Arctic region. Another option is EPSG:3995 (Arctic Polar Stereographic) or the UTM zones, but polar stereographic is best for area measurement across all of Svalbard since UTM zones would split the archipelago. Let me use EPSG:3413 (WGS 84 / NSIDC Sea Ice Polar Stereographic North) which is standard for Arctic area measurements. Actually, EPSG:3995 (WGS 84 / Arctic Polar Stereographic) is also common. Let me check which one is better for Svalbard. EPSG:3413 - standard parallel at 70°N, central meridian at -45° (45°W) — this is commonly used for Arctic EPSG:3995 - WGS 84 / Arctic Polar Stereographic, also common Actually, both should work fine. Let me use EPSG:3413 as it's widely used. Actually wait, let me think more carefully. For Svalbard specifically: - EPSG:3413 (NSIDC Sea Ice Polar Stereographic North): origin at 70°N, 45°W - EPSG:3995 (Arctic Polar Stereographic): origin at 90°N, 0°E Both are suitable. EPSG:3413 is very commonly used for Arctic area calculations. Let me use EPSG:3413. Let me write the script.
0:17
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('svalbard_glaciers_wgs84.gpkg')
print(gdf['name'].head(20))
print(gdf['name'].nunique())
print(gdf['name'].isna().sum())
"
0 Ahlmannfonna 1 Aldegondabreen 2 Altbreen 3 Austgötabreen 4 Austre Brøggerbreen 5 Austre Grønfjordbreen 6 Austre Helvetiabreen 7 AustreLovenbreen 8 Baalsrudbreen 9 Bertilbreen 10 Billesholmbreen 11 Bivrostfonna 12 Bjuvbreen 13 Blackbreen 14 Blekumbreen 15 Bogerbreen 16 Bosarpbreen 17 Brandtbreen 18 Brattvaagbukta 19 Bullbreen Name: name, dtype: str 169 0
0:25
We have 169 glaciers in WGS84 (EPSG:4326). For measuring area at high latitudes (Svalbard is roughly 74°N to 81°N), the best projected coordinate system would be a Polar Stereographic projection. The most appropriate one for Svalbard area would be: 1. EPSG:5936 - WGS 84 / Arctic Polar Stereographic (true scale at 71°N) - this covers the Arctic region 2. EPSG:3413 - WGS 84 / NSIDC Sea Ice Polar Stereographic North 3. EPSG:3995 - WGS 84 / Arctic Polar Stereographic (but centered on 0°E) Actually, for Svalbard specifically, the best option might be EPSG:3413 (NSIDC North Polar Stereographic) or EPSG:5936. Let me think... Actually, EPSG:32631 to EPSG:32639 are UTM zones. Svalbard spans multiple UTM zones: - Zone 31 (3°E to 9°E) - Zone 32 (9°E to 15°E) - Zone 33 (15°E to 21°E) - Zone 34 (21°E to 27°E) - Zone 35 (27°E to 33°E) But UTM at very high latitudes (above 80°N) has significant distortion. For polar regions, the most appropriate CRS is a Polar Stereographic projection. Specifically: EPSG:3413 (NSIDC Sea Ice Polar Stereographic North) - used for Arctic, centered on -45°E, true scale at 70°N EPSG:3995 (Arctic Polar Stereographic) - centered on 0°E, true scale at 71°N EPSG:5936 - WGS 84 / Arctic Polar Stereographic (same as 3995 but with different parameters) Actually, let's look at this more carefully. For area calculations at high latitudes, the best would be: 1. EPSG:3413 - NSIDC Polar Stereographic North - widely used for Arctic sea ice, good for high latitudes 2. EPSG:3995 - WGS 84 / Arctic Polar Stereographic Let me use EPSG:3413 or EPSG:5936. Actually, many sources recommend EPSG:3413 for area calculations in the Arctic. Let me go with EPSG:3413 (NSIDC Sea Ice Polar Stereographic North) which is commonly used for this purpose. Let me verify this choice. Actually, wait - EPSG:3413 is a well-known choice for Arctic area calculations. But let me check if geopandas has it. Let me just write the script.
0:25
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
import pyproj
# Check available CRS definitions
for epsg in [3413, 3995, 5936, 6931]:
try:
crs = pyproj.CRS.from_epsg(epsg)
print(f'EPSG:{epsg} - {crs.name} - area: {crs.area_of_use}')
except:
print(f'EPSG:{epsg} - not available')
"
EPSG:3413 - WGS 84 / NSIDC Sea Ice Polar Stereographic North - area: - name: Northern hemisphere - north of 60°N onshore and offshore, including Arctic. - bounds: (-180.0, 60.0, 180.0, 90.0) EPSG:3995 - WGS 84 / Arctic Polar Stereographic - area: - name: Northern hemisphere - north of 60°N onshore and offshore, including Arctic. - bounds: (-180.0, 60.0, 180.0, 90.0) EPSG:5936 - WGS 84 / EPSG Alaska Polar Stereographic - area: - name: Northern hemisphere - north of 60°N onshore and offshore, including Arctic. - bounds: (-180.0, 60.0, 180.0, 90.0) EPSG:6931 - WGS 84 / NSIDC EASE-Grid 2.0 North - area: - name: Northern hemisphere. - bounds: (-180.0, 0.0, 180.0, 90.0)
0:40
For Svalbard (around 74-81°N), a polar stereographic projection is most appropriate. Let me look at the best ones: - EPSG:3413 - NSIDC Sea Ice Polar Stereographic North (true scale at 70°N) - commonly used for Arctic - EPSG:3995 - Arctic Polar Stereographic (true scale at 71°N) - also good - EPSG:6931 - EASE-Grid 2.0 North EPSG:3413 is the NSIDC polar stereographic, true scale at 70°N, which is quite appropriate for Svalbard. Let me use EPSG:3413. Actually, let me think more carefully. EPSG:3413 (NSIDC Sea Ice Polar Stereographic North) uses: - Latitude of origin: 70°N - Standard parallel: 70°N - Central meridian: -45° This is commonly used for Arctic measurements. EPSG:3995 (Arctic Polar Stereographic) uses: - Latitude of origin: 71°N - Standard parallel: 71°N - Central meridian: 0° For Svalbard (around 10-30°E, 74-81°N), either could work. EPSG:3995 might be slightly better because its central meridian is 0°, closer to Svalbard, and its true scale latitude is 71° which is closer to Svalbard's latitude. But let me just pick one and note it. Actually, EPSG:3413 is the most commonly used for Arctic area measurements. Let me go with that. Let me write a script to: 1. Load the data 2. Project to EPSG:3413 3. Calculate area in km² 4. Get bounding boxes 5. Sort by area descending 6. Output top 20 to CSV
0:40
Write
/work/solve.py
content · 48 lines · py
import geopandas as gpd
import pandas as pd
# Load the data
gdf = gpd.read_file('/work/svalbard_glaciers_wgs84.gpkg')
# The most appropriate CRS for area measurement at high latitudes (Svalbard: ~74-81°N)
# is a polar stereographic projection. EPSG:3413 (NSIDC Sea Ice Polar Stereographic North)
# is widely used for Arctic area measurements.
TARGET_EPSG = 3413
target_crs = f'EPSG:{TARGET_EPSG}'
# Project to the polar CRS
gdf_proj = gdf.to_crs(target_crs)
# Calculate area in square metres then convert to km²
gdf_proj['area_km2'] = gdf_proj.geometry.area / 1e6
# Get bounding boxes in projected coordinates
gdf_proj['bbox_minx_polar'] = gdf_proj.geometry.bounds['minx']
gdf_proj['bbox_miny_polar'] = gdf_proj.geometry.bounds['miny']
gdf_proj['bbox_maxx_polar'] = gdf_proj.geometry.bounds['maxx']
gdf_proj['bbox_maxy_polar'] = gdf_proj.geometry.bounds['maxy']
# Sort by area descending
gdf_sorted = gdf_proj.sort_values('area_km2', ascending=False)
# Take top 20
top20 = gdf_sorted.head(20).copy()
# Build output dataframe
out = pd.DataFrame({
'name': top20['name'],
'area_km2': top20['area_km2'],
'bbox_minx_polar': top20['bbox_minx_polar'],
'bbox_miny_polar': top20['bbox_miny_polar'],
'bbox_maxx_polar': top20['bbox_maxx_polar'],
'bbox_maxy_polar': top20['bbox_maxy_polar'],
'crs_epsg': TARGET_EPSG,
})
# Round area to reasonable precision
out['area_km2'] = out['area_km2'].round(2)
out.to_csv('/work/svalbard_glaciers_top20.csv', index=False)
print("Top 20 glaciers by area (EPSG:3413 - NSIDC Sea Ice Polar Stereographic North):")
print(out.to_string(index=False))
Wrote /work/solve.py (1609 chars).
0:44
Let me run the script.
0:44
Bash
cd /work && python solve.py
Top 20 glaciers by area (EPSG:3413 - NSIDC Sea Ice Polar Stereographic North):
name area_km2 bbox_minx_polar bbox_miny_polar bbox_maxx_polar bbox_maxy_polar crs_epsg
Sørfonna 4147.00 1.018601e+06 -472800.391267 1.093407e+06 -365990.290930 3413
Vestfonna 2272.18 9.559240e+05 -493366.304647 1.014590e+06 -420978.166085 3413
Åsgardfonna 872.25 9.706723e+05 -558393.739427 1.024973e+06 -525034.100104 3413
Kvitøyjøkulen 595.28 1.028270e+06 -251073.056927 1.052280e+06 -211860.173706 3413
Valhallfonna 405.02 9.715117e+05 -527453.984647 1.003139e+06 -505582.963556 3413
Nordmannsfonna 125.60 1.128862e+06 -577716.841074 1.149204e+06 -563508.015625 3413
Sveabreen 121.14 1.042379e+06 -645032.461588 1.070491e+06 -634900.873833 3413
Gruvfonna 109.27 1.144709e+06 -629651.817666 1.156022e+06 -608114.427637 3413
Veteranen 92.38 1.023748e+06 -552570.066540 1.045630e+06 -532772.411813 3413
Himingbjørgfonna 80.51 1.019942e+06 -558249.721488 1.032767e+06 -542067.459536 3413
Hellefonna 78.94 1.137721e+06 -599557.161679 1.153449e+06 -586377.072944 3413
Heuglinbreen 68.23 1.124554e+06 -561227.661019 1.137551e+06 -552367.730797 3413
Eidembreen 64.22 1.067451e+06 -670591.530625 1.073502e+06 -653568.174195 3413
Nordenskiöldbreen 64.19 1.082148e+06 -580288.628158 1.091822e+06 -569000.222702 3413
Isrosa 45.11 1.145942e+06 -580366.488992 1.153648e+06 -567251.221209 3413
Edvardbreen 44.47 1.164649e+06 -614218.850293 1.173314e+06 -600447.124009 3413
Esmarkbreen 41.16 1.075609e+06 -661298.862900 1.088685e+06 -653342.465556 3413
Venernbreen 40.84 1.074999e+06 -669967.183774 1.081719e+06 -657177.431725 3413
Dunérbreen 38.70 9.708777e+05 -526415.352256 9.844084e+05 -519393.154511 3413
Fridtjovbreen 37.60 1.131454e+06 -675329.642301 1.145279e+06 -669881.638282 3413
0:47
The output looks good. Let me verify by reading the CSV file to make sure it's properly formatted.
0:47
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,4147.0,1018600.817882212,-472800.3912665622,1093407.1674378568,-365990.2909299612,3413
3 Vestfonna,2272.18,955923.9805055511,-493366.30464676005,1014589.5375176889,-420978.1660846758,3413
4 Åsgardfonna,872.25,970672.3159938941,-558393.7394266115,1024973.2110542867,-525034.1001042881,3413
5 Kvitøyjøkulen,595.28,1028269.5074980733,-251073.05692732212,1052280.1255731431,-211860.17370643766,3413
6 Valhallfonna,405.02,971511.670097754,-527453.9846470426,1003139.1997554839,-505582.9635556986,3413
7 Nordmannsfonna,125.6,1128862.4515745593,-577716.8410743554,1149204.2654806755,-563508.01562506,3413
8 Sveabreen,121.14,1042378.8994917835,-645032.4615881244,1070491.2293700927,-634900.8738330957,3413
9 Gruvfonna,109.27,1144708.8887145082,-629651.8176660625,1156022.2028977482,-608114.4276372788,3413
10 Veteranen,92.38,1023748.0056298103,-552570.0665400377,1045629.5864769553,-532772.4118134979,3413
11 Himingbjørgfonna,80.51,1019942.2563029992,-558249.7214880211,1032767.2559255561,-542067.4595360316,3413
12 Hellefonna,78.94,1137721.2132937114,-599557.1616786135,1153448.587803471,-586377.0729442405,3413
13 Heuglinbreen,68.23,1124554.481148079,-561227.6610193359,1137550.8359948718,-552367.730796976,3413
14 Eidembreen,64.22,1067450.8900832098,-670591.5306250262,1073502.0562839245,-653568.1741950389,3413
15 Nordenskiöldbreen,64.19,1082148.1499660225,-580288.628157692,1091821.8942982997,-569000.2227019768,3413
16 Isrosa,45.11,1145941.681893076,-580366.4889922002,1153648.207848919,-567251.2212087518,3413
17 Edvardbreen,44.47,1164648.5289905206,-614218.8502931708,1173314.2171653411,-600447.1240089986,3413
18 Esmarkbreen,41.16,1075609.0346814487,-661298.8628999034,1088684.5918938657,-653342.4655558533,3413
19 Venernbreen,40.84,1074998.6217814437,-669967.1837736613,1081719.4183093198,-657177.4317250035,3413
20 Dunérbreen,38.7,970877.7430687764,-526415.3522561246,984408.4476399018,-519393.1545105753,3413
21 Fridtjovbreen,37.6,1131454.2915404618,-675329.6423005203,1145279.019816095,-669881.6382822026,3413
0:55
The output looks good. Let me verify the content and ensure the CSV is well-formed. Let me also double-check the area values are sensible - Sørfonna at 4147 km² seems reasonable for a large ice cap on Svalbard. Let me verify a couple things and maybe check if the values are properly formatted. Actually, looking at the output, the area values seem quite reasonable. Sørfonna and Vestfonna are known as the largest ice caps on Svalbard. The CSV is well-formed with the right columns. Let me also make sure the CRS choice is well-justified. EPSG:3413 (NSIDC Sea Ice Polar Stereographic North) is a standard choice for Arctic area measurements. Another common one is EPSG:3995 (Arctic Polar Stereographic). Both are suitable. Let me also check EPSG:5936 which might be even more appropriate for Svalbard specifically. Actually, looking at the output again - the bbox values seem to have negative y-values which is correct for EPSG:3413 which is a polar stereographic projection. That's fine. Let me verify the CSV is clean.
0:55
0:55
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
import pandas as pd
# Quick sanity check: compare areas with EPSG:3995 (Arctic Polar Stereographic)
gdf = gpd.read_file('svalbard_glaciers_wgs84.gpkg')
# Try EPSG:5936 (more Svalbard-specific) and EPSG:3413
for epsg, label in [(3413, 'NSIDC North'), (3995, 'Arctic Polar Stereo'), (5936, 'Alaska Polar Stereo')]:
gdf_proj = gdf.to_crs(f'EPSG:{epsg}')
area = gdf_proj.geometry.area / 1e6
# Check Sørfonna (index 0 in sorted order)
idx = gdf['name'] == 'Sørfonna'
print(f'{label} (EPSG:{epsg}): Sørfonna area = {area[idx].values[0]:.2f} km²')
# Also check the file
df = pd.read_csv('/work/svalbard_glaciers_top20.csv')
print(f'\nCSV rows: {len(df)}')
print(f'Columns: {list(df.columns)}')
print(f'CRS EPSG: {df[\"crs_epsg\"].unique()}')
print(f'Total area sum: {df[\"area_km2\"].sum():.2f} km²')
"
NSIDC North (EPSG:3413): Sørfonna area = 4147.00 km² Arctic Polar Stereo (EPSG:3995): Sørfonna area = 4171.93 km² Alaska Polar Stereo (EPSG:5936): Sørfonna area = 4356.02 km² CSV rows: 20 Columns: ['name', 'area_km2', 'bbox_minx_polar', 'bbox_miny_polar', 'bbox_maxx_polar', 'bbox_maxy_polar', 'crs_epsg'] CRS EPSG: [3413] Total area sum: 9344.09 km²
1:01
The results look reasonable. EPSG:3413 (NSIDC Sea Ice Polar Stereographic North) is the most widely accepted choice for Arctic area measurements, especially for high-latitude regions like Svalbard (~74-81°N). It's specifically designed for the northern hemisphere above 60°N and is the standard for Arctic sea ice and glaciological studies. The file is ready. Let me provide a summary.
1:01