Bibliographic record
Abstract
DEMIX Database version 4.1 Data created for publication: Guth, P.L.; Trevisani, S.; Grohmann, C.H.; Lindsay, J.B. Reply to Ho et al. Comment on “Guth et al. Benchmarking Elevation Plus Land Surface Parameters Finds FathomDEM and Copernicus DEM Win as Best Global DEMs. Remote Sens. 2025, 17, 3919”. Remote Sens. 2026, 18, 2399. https://doi.org/10.3390/rs18142399 This database supports the work of the Digital Elevation Model Intercomparison eXperiment (DEMIX) working group (Strobl and others, 2021) and particularly the previous comparison efforts (Guth and others, 2021; Bielski and others, 2024; Guth and others, 2024; Guth and others, 2025). This version only includes 626 new test tiles, and may have biases because the tiles were selected to add diversity to the version 4 database. It should be used in conjunction with the full version 4 (Guth, 2025). Database changes: Tiles names are taken from the source HRDEMs. This avoids required separate code for every national mapping agency (and some, like the USGS, currently have at least 3 naming standards for their tiles). It also includes the survey date for some countries (currently only the USA). Added three additional fields to the tile characteristics: Koppen climate classification (Rube and Kottek, 2010), Biome Name (Copernicus Land Monitoring Service, 2017) and average canopy height from the laser radar altimeters (NASA, 2026). Three additional criteria in the mixed FUV database: KNCC, KPS, and MAD2. One new LSP in the difference distribution data: TAN Add collections to group test tiles in terms of when they were added to the analysis, and the reason. This database includes these new collections with new test tiles: o Urban areas (107 tiles): these are problematic with 10x10 km tiles, since urban areas typically do not match the HRDEM tiles. o Tropical areas (195 tiles). This greatly increases the representation of tropical forests, as measured by both the biome and the canopy height metrics. o New (276 tiles). This adds a number of tiles from France and Canada, and a few new ones from the USA. o Spanish deserts (48 tiles). Version 4 only had desert tiles in the USA and the Canary Islands, so this attempts to add more geographic diversity. This database contains the following tables: hrdem_source_ref.html: download and license details for the HRDEMs used to create reference DTM table_GDEM_download_license.html: download and license details for the test global DEMs demix_db_4.0_tile_characteristics_with_added_characteristics.csv : tile characteristics for the Guth (2025) database with the three new characteristics added demix_db_4.1_tile_stats_DB_04_14_26.csv: tile characteristics for the new test tiles demix_db_4.1_diff_distrib_DB_04_14_26.csv: results from the difference distribution criteria for the new test tiles demix_db_4.1_mixed_fuv_DB_04_14_26.csv: results from the FUV criteria for the new test tiles · References: Bielski, C.; López-Vázquez, C.; Grohmann, C.H.; Guth. P.L.; Hawker, L.; Gesch, D.; Trevisani, S.; Herrera-Cruz, V.; Riazanoff, S.; Corseaux, A.; Reuter, H.; Strobl, P., 2024. Novel approach for ranking DEMs: Copernicus DEM improves one arc second open global topography. IEEE Transactions on Geoscience & Remote Sensing. vol. 62, pp. 1-22, 2024, Art no. 4503922, https://doi.org/10.1109/TGRS.2024.3368015 Copernicus Land Monitoring Service. (2017) Welcome to Ecoregions 2017©Resolve. Available online: https://ecoregions.appspot.com/ (accessed on 9 April 2026). Guth, P. (2025). DEMIX GIS Database Version 4 (Version 4) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17538186 Guth, P.L.; Trevisani, S.; Grohmann, C.H.; Lindsay, J.; Gesch, D.; Hawker, L.; Bielski, C. Ranking of 10 Global One-Arc-Second DEMs Reveals Limitations in Terrain Morphology Representation. Remote Sens. 2024, 16, 3273. https://doi.org/10.3390/rs16173273 Guth, P.L.; and others 2025 (others being Trevisani, S.; Grohmann, C.H.; Lindsay, J.B.; Reuter, H.I. ) Benchmarking Elevation Plus Land Surface Parameters Finds FathomDEM and Copernicus DEM Win as Best Global DEMs. Remote Sens. 2025, 17, 3919. https://doi.org/10.3390/rs17233919 Guth, P.L.; Van Niekerk, A.; Grohmann, C.H.; Muller, J.-P.; Hawker, L.; Florinsky, I.V.; Gesch, D.; Reuter, H.I.; Herrera-Cruz, V.; Riazanoff, S.; López-Vázquez, C.; Carabajal, C.C.; Albinet, C.; Strobl, P. Digital Elevation Models: Terminology and Definitions. Remote Sens. 2021, 13, 3581. https://doi.org/10.3390/rs13183581 NASA. (2026) Global Vegetation Height Metrics from GEDI and ICESat2. Available online: https://data.nasa.gov/dataset/global-vegetation-height-metrics-from-gedi-and-icesat2-71ab5 (accessed on 9 April 2026). Rubel, F., and M. Kottek, 2010: Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorol. Z., 19, 135-141. DOI: 10.1127/0941-2948/2010/0430. Strobl, P.A.; Bielski, C.; Guth, P.L.; Grohmann, C.H.; Muller, J.P.; López-Vázquez, C.; Gesch, D.B.; Amatulli, G.; Riazanoff, S.; Carabajal, C. The Digital Elevation Model Intercomparison eXperiment DEMIX, a community based approach at global DEM benchmarking. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2021, XLIII-B4-2021, 395–400. https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.173 | 0.089 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".