The ALOS Global Digital Surface Model (AW3D30) is a global dataset generated from images collected using the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) aboard the Advanced Land Observing Satellite (ALOS) from 2006 to 2011. As described by the Japan Aerospace Exploration Agency: The Japan Aerospace Exploration Agency (JAXA) releases the global digital surface model (DSM) dataset with a horizontal resolution of approx. 30-meter mesh (1 arcsec) free of charge. The dataset has been compiled with images acquired by the Advanced Land Observing Satellite "DAICHI" (ALOS). The dataset is published based on the DSM dataset (5-meter mesh version) of the "World 3D Topographic Data", which is the most precise global-scale elevation data at this time, and its elevation precision is also at a world-leading level as a 30-meter mesh version. This dataset is expected to be useful for scientific research, education, as well as the private service sector that uses geospatial information.

Version: As of May 24th 2021 OpenTopography is supplying V3.2 (Jan 2021) from:
ftp://ftp.eorc.jaxa.jp//pub/ALOS/ext1/AW3D30/release_v2012_single_format/
Data downloaded prior to May 24th 2021 was in format: May 2016: Global terrestrial region (within approx. 82 deg. of N/S latitudes) of Version 1 released (approx. 22,100 tiles)

Note: JAXA provides two versions of AW3D30 created from the original 5-meter mesh using different downsampling methods: average (provided here) and median (not available from OpenTopography).

ALOS World 3D - 30m (AW3D30) is a global digital surface model (DSM) dataset with a horizontal resolution of approximately 30 meters (1 arcsec mesh). The dataset is based on the DSM dataset (5-meter mesh version) of the World 3D Topographic Data. More details are available in the dataset documentation. This ingested dataset combines data from versions 3.1, 4.0, and 4.1. Version 4.1 (April 2024): This major update released 19,051 tiles covering global regions (excluding Antarctica and Japan). It incorporates new supplementary data for void filling and corrects partial anomalies found in versions 3.1 and 3.2, along with re-filling voids. For specific tile updates in v4.1, please use the v4.1 filter on map tiles or consult the latest format description. Version 4.0 (April 2023): This update released 1,886 tiles, improving low and mid-latitude regions and areas south of 60 degrees latitude. Key changes include: 1. New supplementary data for void filling. 2. Correction of partial anomalies and re-filling of voids (2 tiles). 3. Updated coastlines for regions south of 60 degrees latitude (44 tiles). 4. Disabled Caspian Sea water mask and supplemented with elevation data (54 tiles). 5. Extracted and corrected new partial anomaly areas in South America (1,786 tiles). 6. For detailed tile information for v4.0, please use the v4.0 filter on map tiles or refer to the format description. Version 3.2, released in January 2021, is an improved version created by reconsidering the format in the high latitude area, auxiliary data, and processing method. Different pixel spacing for each latitude zone was adopted at high latitude area. Coastline data, which is one of the auxiliary datasets, was changed, and new supplementary data was used. In addition, as a source data for Japan, AW3D version 3 was also used. Furthermore, the method of detecting anomalous values in the process was improved. Note: See the code example for the recommended way of computing slope. Unlike most DEMs in Earth Engine, this is an image collection due to multiple resolutions of source files that make it impossible to mosaic them into a single asset, so the slope computations need a reprojection. The AW3D DSM elevation is calculated by an image matching process that uses a stereo pair of optical images. Clouds, snow, and ice are automatically identified during processing and applied the mask information. However, mismatched points sometimes remain especially surrounding (or at the edges of) clouds, snow, and ice areas, which cause some height errors in the final DSM.

Note: This version of the ALOS World 3D dataset is provided in a WGS84 ellipsoidal vertical datum. This is a non-standard version of the AW3D30 dataset that is often used for InSAR analysis. This dataset was converted from the orthometric version using the EGM96 geoid model available here. For additional conversion details feel free to contact us.

The ALOS Global Digital Surface Model (AW3D30) is a global dataset generated from images collected using the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) aboard the Advanced Land Observing Satellite (ALOS) from 2006 to 2011. As described by the Japan Aerospace Exploration Agency: The Japan Aerospace Exploration Agency (JAXA) releases the global digital surface model (DSM) dataset with a horizontal resolution of approx. 30-meter mesh (1 arcsec) free of charge. The dataset has been compiled with images acquired by the Advanced Land Observing Satellite "DAICHI" (ALOS). The dataset is published based on the DSM dataset (5-meter mesh version) of the "World 3D Topographic Data", which is the most precise global-scale elevation data at this time, and its elevation precision is also at a world-leading level as a 30-meter mesh version. This dataset is expected to be useful for scientific research, education, as well as the private service sector that uses geospatial information.

Version: As of May 24th 2021 OpenTopography is supplying V3.2 (Jan 2021) from:
ftp://ftp.eorc.jaxa.jp//pub/ALOS/ext1/AW3D30/release_v2012_single_format/
Data downloaded prior to May 24th 2021 was in format: May 2016: Global terrestrial region (within approx. 82 deg. of N/S latitudes) of Version 1 released (approx. 22,100 tiles)

Note: JAXA provides two versions of AW3D30 created from the original 5-meter mesh using different downsampling methods: average (provided here) and median (not available from OpenTopography).

This study developed a method to estimate building height for all of China based on the Advanced Land Observing Satellite (ALOS) World 3D-30 m (AW3D30) DSM and other ancillary data including the Global Artificial Impervious Area (GAIA) dataset, the NASADEM dataset and the Global Roads Inventory Project (GRIP) dataset. The proposed method enabled us to accurately estimate building height with a special slope correction algorithm, improving the accuracy of building height estimation. The outcome of our procedure is a map of building height for China at a spatial resolution of 30 m. Compared to field-measured building height data and reference building height data from Baidu map, results indicate that the proposed method performed well (root mean square error (RMSE) of 4.26 m and 4.98 m, respectively). The new building height map of China contributes to the improved management of urban areas and further studies of urban environments.Reference: https://doi.org/10.1016/j.isprsjprs.2022.01.022.

ALOS World 3D - 30m (AW3D30) 是一项全球数字表面模型(DSM) 数据集，水平分辨率约为30 米（1 角秒网格）。该数据集基于世界3D 地形数据的DSM 数据集（5 米网格版本）。如需了解详情，请参阅数据集文档。…

ALOS World 3D – 30m‏ (AW3D30) הוא מערך נתונים של מודל דיגיטלי גלובלי של פני השטח (DSM) עם רזולוציה אופקית של כ-30 מטרים (רשת של 1 שניות קשת). מערך הנתונים מבוסס על מערך הנתונים של DSM (גרסת רשת של 5 מטרים) של World 3D Topographic Data. פרטים נוספים זמינים במסמכי התיעוד של מערך הנתונים. מערך הנתונים שעבר הטמעה משללב נתונים מגרסאות 3.1,‏ 4.0 ו-4.1. גרסה 4.1 (אפריל 2024): במסגרת העדכון העיקרי הזה נוספו 19,051 משבצות שכוללות אזורים ברחבי העולם (לא כולל אנטארקטיקה ויפן). הגרסה הזו כוללת נתונים משלימים חדשים למילוי נתונים חסרים, מתקנת חריגות חלקיות שנמצאו בגרסאות 3.1 ו-3.2, וממלאת מחדש נתונים חסרים. כדי לקבל עדכונים ספציפיים של משבצות בגרסה 4.1, צריך להשתמש במסנן v4.1 על משבצות המפה או לעיין בתיאור הפורמט העדכני ביותר. גרסה 4.0 (אפריל 2023): בעדכון הזה נוספו 1,886 משבצות, ששיפרו את האיכות באזורים עם קו הרוחב נמוך ובינוני ובאזורים שמדרום לקו הרוחב 60 מעלות. השינויים העיקריים כוללים: 1. נתונים משלימים חדשים למילוי חוסרים. 2. תיקון של חריגות חלקיות ומילוי מחדש של שטחים ריקים (2 משבצות). 3. קו החוף עודכן באזורים שמדרום לקו הרוחב 60 מעלות (44 משבצות). 4. מסכת המים של הים הכספי מושבתת ומתווספים לה נתוני גובה (54 משבצות). 5. חילוץ ותיקון של אזורים חדשים של חריגות חלקיות בדרום אמריקה (1,786 משבצות). 6. למידע מפורט על המשבצות של גרסה 4.0, אפשר להשתמש במסנן v4.0 על משבצות המפה או לעיין בתיאור הפורמט. גרסה 3.2, שפורסמה בינואר 2021, היא גרסה משופרת שנוצרה על ידי בדיקה מחדש של הפורמט באזורים עם קו הרוחב הגבוה, של הנתונים המשניים ושל שיטת העיבוד. באזורים עם קו הרוחב גבוה, הוחלט להשתמש במרווחים שונים של פיקסלים לכל אזור רוחב. נתוני קו החוף, שהם אחת מקבוצות הנתונים המשניות, השתנו, והשתמשו בנתונים משלימים חדשים. בנוסף, נעשה שימוש גם ב-AW3D גרסה 3 כנתוני מקור ליפן. בנוסף, שיפרנו את השיטה לזיהוי ערכים חריגים בתהליך. הערה: בדוגמה לקוד מוסבר איך מחשבים את השיפוע בצורה המומלצת. בניגוד לרוב ה-DEM ב-Earth Engine, מדובר באוסף תמונות בגלל שיש כמה רזולוציות של קובצי המקור, ולכן אי אפשר ליצור מהם מוזיאה לנכס יחיד. לכן, כדי לחשב את השיפועים צריך לבצע הקרנה מחדש. הגובה של DSM ב-AW3D מחושב באמצעות תהליך התאמת תמונות שמשתמש בזוג תמונות אופטיות סטריאוסקופיות. עננים, שלג וקרח מזוהים באופן אוטומטי במהלך העיבוד, ומידע מהמסכה מוחל עליהם. עם זאת, לפעמים נשארות נקודות לא תואמות, במיוחד סביב עננים, שלג ואזורי קרח (או בקצוות שלהם), שגורמות לשגיאות גובה מסוימות ב-DSM הסופי.

A dataset of pairs of RGB satellite images and their corresponding DEM images.

ALOS World 3D - 30m (AW3D30)는 가로 해상도가 약 30m (1아르크초 메시)인 전 세계 디지털 표면 모델 (DSM) 데이터 세트입니다. 이 데이터 세트는 세계 3D 지형 데이터의 DSM 데이터 세트(5m 메시 버전)를 기반으로 합니다. 자세한 내용은 데이터 세트 문서를 참고하세요. 처리된 이 데이터 세트는 버전 3.1, 4.0, 4.1의 데이터를 결합합니다. 버전 4.1 (2024년 4월): 이 주요 업데이트에서는 전 세계 지역 (남극과 일본 제외)을 다루는 19,051개의 타일을 출시했습니다. 이 버전은 공백 채우기를 위한 새로운 보조 데이터를 통합하고 버전 3.1 및 3.2에서 발견된 부분적 이상치를 수정하고 공백을 다시 채웁니다. v4.1의 특정 카드 업데이트의 경우 지도 카드에서 v4.1 필터를 사용하거나 최신 형식 설명을 참고하세요. 버전 4.0 (2023년 4월): 이 업데이트에서는 1,886개의 타일을 출시하여 저위도 및 중위도 지역과 위도 60도 남쪽의 지역을 개선했습니다. 주요 변경사항은 다음과 같습니다. 1. 공백 채우기를 위한 새로운 보조 데이터 2. 부분적인 이상치를 수정하고 공백을 다시 채웁니다 (타일 2개). 3. 위도 60도 남쪽 지역의 해안선을 업데이트했습니다 (44개 타일). 4. 카스피 해 수역 마스킹을 사용 중지하고 고도 데이터(54개 타일)로 보완했습니다. 5. 남미의 새로운 부분적 이상 영역(1,786개 타일)을 추출하여 수정했습니다. 6. v4.0의 자세한 카드 정보는 지도 카드에서 v4.0 필터를 사용하거나 형식 설명을 참고하세요. 2021년 1월에 출시된 버전 3.2는 고위도 지역의 형식, 보조 데이터, 처리 방법을 재고하여 만든 개선된 버전입니다. 고위도 지역에서는 위도 영역별로 다른 픽셀 간격이 채택되었습니다. 보조 데이터 세트 중 하나인 해안선 데이터가 변경되고 새로운 보조 데이터가 사용되었습니다. 또한 일본의 소스 데이터로 AW3D 버전 3도 사용되었습니다. 또한 프로세스에서 이상 값을 감지하는 방법이 개선되었습니다. 참고: 경사 계산의 권장 방법은 코드 예시를 참고하세요. Earth Engine의 대부분의 DEM과 달리 이 DEM은 소스 파일의 해상도가 여러 개여서 단일 애셋으로 모자이크 처리할 수 없으므로 이미지 모음입니다. 따라서 경사 계산에는 재프로젝션이 필요합니다. AW3D DSM 고도는 광학 이미지의 스테레오 쌍을 사용하는 이미지 일치 프로세스로 계산됩니다. 처리 중에 구름, 눈, 얼음이 자동으로 식별되고 마스크 정보가 적용됩니다. 그러나 일치하지 않는 지점이 특히 구름, 눈, 얼음 지역 주변 (또는 가장자리)에 남아 최종 DSM에 일부 고도 오류가 발생하는 경우가 있습니다.

Elevation data set from AW3D30 DEM for two location within Nigeria:Lagos state and Abuja

The data is from JAXA earth observation research center（ http://www.eorc.jaxa.jp/ALOS/en/aw3d30/ ）The product is alos World 3D - 30m (aw3d30). Select and download the map by importing the SHP boundary of Sichuan Tibet traffic corridor, and merge it into one by using relevant software. The format is raster data, the spatial resolution is 30m, and the data size is 1.3GB. The DEM data can generate topographic factor data such as slope, aspect and river network by using relevant software. They are the basic data for topographic analysis of Sichuan Tibet traffic corridor, help to understand the geomorphic form of the basin, and are also the key factors for disaster zoning research and risk assessment. The acquisition of high-precision DEM is of great significance for disaster risk management and decision-making level and reducing the loss of major geological disasters.

Compilation and harmonization of environmental covariates for PSM in Canada Developed for: Agriculture and Agri-Food Canada/Agriculture et Agroalimentaire Canada.

Layers (see: CanSIS explanation of names):

• ALOS AW3D30 elevation model (canopy height),

Prepared by: Robert A. MacMillan (bobmacm@gmail.com; LandMapper Environmental Solutions Inc.) and Tom Hengl (EnvirometriX Ltd)

إنّ بيانات الارتداد المعياري التي تبلغ مساحتها 25 مترًا والمستخرَجة من نظام PALSAR-2 ScanSAR هي بيانات مستخرَجة من وضع المراقبة على نطاق واسع في نظام PALSAR-2، ويبلغ عرض المراقبة 350 كيلومترًا. تم تصحيح صور الرادار ذي الفتحة الاصطناعية (SAR) جغرافيًا وتصحيحها حسب الانحدار باستخدام نموذج السطح الرقمي ALOS World 3D - 30 m (AW3D30). يتم تخزين بيانات الاستقطاب كأرقام رقمية (DN) ذات 16 بت. …

陸域観測技術衛星「ALOS」搭載のパンクロマチック立体視センサ (PRISM) による全球数値地表モデル (DSM) の30m相当 (1arcsec) 解像度版データセットを無償公開します。 / 本データセットは、全球規模で整備される標高データセットとして現時点で世界最高精度を持つ「全世界デジタル3D地形データ」のDSMデータセット (5m相当解像度) をベースとして作成しています。JAXAは科学研究分野や地理空間情報を活用したサービス等に広く利用していただくために水平解像度30mデータセットを無償で公開します。【リソース】ALOS全球数値地表モデル (DSM) イメージ画像 / ALOS全球数値地表モデル (DSM) "ALOS World 3D - 30m" (AW3D30)のイメージ画像です。 / ALOS全球数値地表モデル (DSM) "ALOS World 3D - 30m" (AW3D30)【キーワード】3D / ALOS / DSM

Les données de rétrodiffusion normalisées de la couverture ScanSAR PALSAR-2 de 25 mètres sont issues du mode d'observation à large zone de PALSAR-2, avec une largeur d'observation de 350 km. L'imagerie SAR a été orthorectifiée et corrigée en fonction de la pente à l'aide du modèle numérique de surface ALOS World 3D - 30 m (AW3D30). Les données de polarisation sont stockées sous forme de nombres numériques de 16 bits. Les valeurs DN peuvent être converties en valeurs gamma naught en décibels (dB) à l'aide de l'équation suivante : γ0 = 10*log10(DN2) - 83,0 dB Les données de niveau 2.2 sont orthorectifiées et corrigées radiométriquement en fonction du terrain. Cet ensemble de données est compatible avec la norme Committee on Earth Observation (CEOS) Analysis Ready Data for LAND (CARD4L).

Digital Terrain Model for Continental Europe based on the three publicly available Digital Surface Models and predicted using an Ensemble Machine Learning (EML). EML was trainined using GEDI level 2B points (Level 2A; "elev_lowestmode") and ICESat-2 (ATL08; "h_te_mean"): about 9 million points were overlaid vs MERITDEM, AW3D30, GLO-30, EU DEM, GLAD canopy height, tree cover and surface water cover maps, then an ensemble prediction model (mlr package in R) was fitted using random forest, Cubist and GLM, and used to predict most probable terrain height (bare earth). Input layers used to train the EML include:

• "lcv_bare.earth_glcf.landsat": UMD GLAD bare earth estimate for year 2010 based on Landsat time series,
• "dtm_elev.dsm_alos.aw3d": Digital Surface Model based on ALOS AW3D,
• "dtm_canopy.height_glad.umd": UMD GLAD canopy height for 2019 based on GEDI data,
• "dtm_elev.dsm_eudem.eea": Copernicus EU DEM based on the SRTM and ASTER DEMs,
• "hyd_surface.water_jrc.gswe": JRC Global Surface Water Explorer surface water probability based on the Landsat time-series,
• "lcv_landcover.12_pflugmacher2019": land cover map of Europe at 30 based on Pflugmacher et al. (2019),
• "lcv_tree.cover_umd.landsat_2000": forest tree cover for year 2000 based on the Global Forest Change data,
• "lcv_tree.cover_umd.landsat_2010": forest tree cover for year 2010 based on the Global Forest Change data,

Detailed processing steps can be found here. Read more about the processing steps here.

Training data set can be obtained in the file "gedi_elev.lowestmode_2019_eumap.RDS". The initial linear model fitted using the four independent Digital Surface / Digital Terrain models shows:

Residuals:
 Min    1Q  Median    3Q   Max 
-124.627  -1.097  0.973  2.544  59.324 
 
Coefficients:
 Estimate Std. Error t value Pr(>|t|)  
(Intercept)     -1.6220640 0.0032415 -500.4  <2e-16 ***
 eu_dem25m_     -0.1092988 0.0005531 -197.6  <2e-16 ***
 eu_AW3Dv2012_30m_  0.0933774 0.0005957  156.7  <2e-16 ***
 eu_GLO30_30m_    0.2637153 0.0006062  435.1  <2e-16 ***
 eu_MERITv1.0.1_30m_ 0.7496494 0.0005009 1496.6  <2e-16 ***
 ---
 Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 7.059 on 9588230 degrees of freedom
(2046196 observations deleted due to missingness)
Multiple R-squared: 0.9996,  Adjusted R-squared: 0.9996 
F-statistic: 5.343e+09 on 4 and 9588230 DF, p-value: < 2.2e-16

Which show that MERIT DEM (Yamazaki et al., 2019) is the most correlated DEM with GEDI and ICESat-2, most likely because it has been systematically post-processed and majority of canopy problems have been removed. Summary results of the model training (mlr::makeStackedLearner) using all covariates (including canopy height, tree cover, bare earth cover) shows:

Variable: elev_lowestmode.f 
R-square: 1 
Fitted values sd: 333 
RMSE: 6.54 

Ensemble model:
Call:
stats::lm(formula = f, data = d)

Residuals:
   Min    1Q  Median    3Q   Max 
-118.788  -0.871  0.569  1.956 165.119 

Coefficients:
       Estimate Std. Error t value Pr(>|t|)  
(Intercept) -0.198402  0.003045 -65.15  <2e-16 ***
regr.ranger 0.452543  0.001117 405.04  <2e-16 ***
regr.cubist 0.527011  0.001516 347.61  <2e-16 ***
regr.glm   0.020033  0.001217  16.47  <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 6.544 on 9588231 degrees of freedom
Multiple R-squared: 0.9996,  Adjusted R-squared: 0.9996 
F-statistic: 8.29e+09 on 3 and 9588231 DF, p-value: < 2.2e-16

Which indicates that the elevation errors are in average (2/3rd of pixels) between +1-2 m. The variable importance based on Random Forest package ranger shows:

Variable importance:
        variable  importance
4  eu_MERITv1.0.1_30m_ 430641370770
1   eu_AW3Dv2012_30m_ 291483345389
2     eu_GLO30_30m_ 201517488587
3      eu_dem25m_ 132742500162
9 eu_canopy_height_30m_  5148617173
7       bare2010_  2087304901
8    treecover2000_  1761597272
6    treecover2010_  141670217

The output predicted terrain model includes the following two layers:

• "dtm_elev.lowestmode_gedi.eml_mf": mean estimate of the terrain elevation in dm (decimeters) filtered using Gaussian filter and 2x pixel window in SAGA GIS,
• "dtm_elev.lowestmode_gedi.eml_md": standard deviation of the independently fitted stacked predictors quantifying the prediction uncertainty in dm (decimeters),

The predicted elevations are based on the GEDI data hence the reference water surface (WGS84 ellipsoid) is about 43 m higher than the sea water surface for a specific EU country. Before modeling, we have corrected the reference elevations to the Earth Gravitational Model 2008 (EGM2008) by using the 5-arcdegree resolution correction surface (Pavlis et al, 2012).

All GeoTIFFs were prepared using Integer format (elevations rounded to 1 m) and have been converted to Cloud Optimized GeoTIFFs using GDAL.

Disclaimer: The output DTM still shows forest canopy (overestimation of the terrain elevation) and has not been hydrologically corrected for spurious sinks and similar. This data set is continuously updated. To report a bug or suggest an improvement, please visit here. To access DTM derivatives at 30-m, 100-m and 250-m please visit here. To register for updates please subscribe to: https://twitter.com/HarmonizerGeo.

The commonly used topographic data in the Himalayas is the SRTM DEM 30m resolution data, but this data was obtained in 2000 with relatively poor timeliness. In order to update the topographic data in this region, the AW3D (ALOS World 3D) 30m DEM data covering this region was obtained. The data was produced by the Japan Aerospace Exploration Agency, or JAXA. The data processed by ALOS (Advanced Land Observing Satellite) is the optical stereoscopic pair data collected by the Panchromatic Remote Sensing Stereoscopic Mapper (PRISM) from 2006 to 2011. In May 2015, free products were provided to the world. The data has a horizontal resolution of 30m (1 arcsecond) and an elevation accuracy of 5m, making it one of the most accurate topographic data in the world. The data download address to https://www.eorc.jaxa.jp/ALOS/en/aw3d30/data/.

O PALSAR-2 ScanSAR de 25 m são dados de retroespalhamento normalizados do modo de observação de área ampla do PALSAR-2 com largura de observação de 350 km. As imagens de SAR foram ortorretificadas e corrigidas por inclinação usando o modelo digital de superfície ALOS World 3D - 30 m (AW3D30). Os dados de polarização são armazenados como números digitais de 16 bits (DN). …

Layers include: Ensemble Digital Terrain Model (EDTM) in 250-m resolution. Unit is in metre(m) and precision is in decimetre (dm). Maps are downscaled from 30-m resolution to 250-m in order to fit the size limit. We provide 30-m EDTM and its standard deviation as links:

• 30-m EDTM

https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_p10_30m_s_2018_go_epsg4326_v20230221.tif

• Standard deviation

https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_std_30m_s_2018_go_epsg4326_v20230221.tif

Derived using ALOS AW3D, GLO-30, MERITDEM, and national DTMs. We derived a lower 10% quantile from all maps. In order to create bare earth data, we used canopy height (canopy height > 2m) and standard deviation (sd > 6m) to mask building and forest in AW3D and GLO-30. Practical processing is written here in Python.

To access and visualize maps use: OpenLandMap.org

If you discover a bug, artifact or inconsistency, or if you have a question please use some of the following channels:

• Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues
• General questions and comments: https://disqus.com/home/forums/landgis/

All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL in Cloud Optimised GeoTiff (COG). File naming convention:

• dtm.bareearth = variable: digital terrain model (m), bare earth
• ensemble = determination method: ensemble of mutli-source dsm and dtm
• p10/std = aggregation/statistics method: 10th percentile / standard deviation
• 250m = spatial resolution / block support: 250 m,
• s = vertical reference: at surface,
• go = bounding box: global land without Antarctica
• epsg.4326 = ESPG code: epsg.4326
• v20230221 = version code: creation date 20230221

This database supports the work of the Digital Elevation Model Intercomparison eXperiment (DEMIX) working group (Strobl and others, 2021; Guth and others, 2021; Bielski and others, 2023, 2024). The two files have the database in CSV format, and a metadata file describing the contents of each field in the database.

To understand the use of the database, see the prepint (Bielski and others, 2023).

Changes to version 2 which is the only version you should use:

1. Added 2 new areas, Stateline and Canary Islands East which should have minimal differences between the DSM and the DTM and no significant changes over the last 20 years.

2. Added the country to the database

3. Added a number of areas in France

4. Added some additional tiles for a few areas

5. Total number of tiles almost doubled

6. Now using GDAL to compute the datum shift, horizontal and vertical, for USGS 3DEP

7. Fixed some anomalies computing pixel-is-area DEMs

8. Recomputed all the reference data and the version 1.0 GIS database (Guth, 2022)

9. New file naming conventions

References:

Bielski, C.; López-Vázquez, C.; Guth. P.L.; Grohmann, C.H. and the TMSG DEMIX Working Group, 2023. DEMIX Wine Contest Method Ranks ALOS AW3D30, COPDEM, and FABDEM as Top 1” Global DEMs: https://arxiv.org/pdf/2302.08425.pdf

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

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

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

PALSAR-2 ScanSAR का 25 मीटर का बैकस्कैटर डेटा, PALSAR-2 के बड़े इलाके के ऑब्ज़र्वेशन मोड का नॉर्मलाइज़ किया गया डेटा है. इस मोड में ऑब्ज़र्वेशन की चौड़ाई 350 कि॰मी॰ होती है. SAR इमेज को ऑर्थो-रेक्टिफ़ाइड किया गया था और ALOS World 3D - 30 मीटर (AW3D30) डिजिटल सर्फ़ेस मॉडल का इस्तेमाल करके, ढलान में सुधार किया गया था. पोलराइज़ेशन डेटा को 16-बिट डिजिटल नंबर (डीएन) के तौर पर सेव किया जाता है. …