3D Spatial Data Processing

Data publications of the 3D Spatial Data Processing Group at the Institute of Geography at Heidelberg University.

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1 to 10 of 126 Results

An Automatic Iterative Random Forest approach to derive gully activity maps in large areas with training data scarcity [Data and Source Code] May 13, 2024 Vallejo-Orti, Miguel; Winiwarter, Lukas; Corral, Eva; Williams, Jack; Bubenzer, Olaf; Höfle, Bernhard, 2024, "An Automatic Iterative Random Forest approach to derive gully activity maps in large areas with training data scarcity [Data and Source Code]", https://doi.org/10.11588/data/WGAU4Q, heiDATA, V1 Gullies are landforms with specific patterns of shape, topography, hydrology, vegetation, and soil characteristics. Remote sensing products (TanDEM-X, Sentinel-1, and Sentinel-2) serve as inputs into an iterative algorithm, initialized using a micro-mapping simulation as training...
Codes.zip May 13, 2024 - An Automatic Iterative Random Forest approach to derive gully activity maps in large areas with training data scarcity [Data and Source Code] ZIP Archive - 12.9 KB - MD5: cb55379c51c6a3587ab720087b9972ef Code
Inputs.zip May 13, 2024 - An Automatic Iterative Random Forest approach to derive gully activity maps in large areas with training data scarcity [Data and Source Code] Shapefile as ZIP Archive - 7.1 KB - MD5: 2d6e7c4f81930ea039e86b5d39761c0b Data
MetaData_iterative_random_forest.txt May 13, 2024 - An Automatic Iterative Random Forest approach to derive gully activity maps in large areas with training data scarcity [Data and Source Code] Plain Text - 5.9 KB - MD5: 03e491ffe111e2ad1f3e2bb834e2f0af Codes metadata
Outputs.zip May 13, 2024 - An Automatic Iterative Random Forest approach to derive gully activity maps in large areas with training data scarcity [Data and Source Code] Shapefile as ZIP Archive - 876.5 KB - MD5: f492ebb9c945d58254d338d7d6118236 Data
Integrating VGI contributions for gully mapping using Kalman filter and machine learning Feb 19, 2024 Vallejo Orti, Miguel; Anders, Katharina; Ajali, Oliubikum; Bubenzer, Olaf; Höfle, Bernhard, 2024, "Integrating VGI contributions for gully mapping using Kalman filter and machine learning", https://doi.org/10.11588/data/UHSQG0, heiDATA, V1, UNF:6:dbfZe/C8CmWXcBEZJg2RPw== [fileUNF] The codes and datsets included are related to experiments and results conducted to integrate different lines digitized by volunteers using Kalman filter with changing amount of input lines. Three approaches are included: i) Kalman filtering integration to investigate the role of...
Cross_Training_Integration.zip Feb 19, 2024 - Integrating VGI contributions for gully mapping using Kalman filter and machine learning ZIP Archive - 1.9 MB - MD5: 4833cdc615e69607bd51b49cfff7ea9d
Kalman_filter_integration.py Feb 19, 2024 - Integrating VGI contributions for gully mapping using Kalman filter and machine learning Python Source Code - 23.7 KB - MD5: 9b1919e5bf0dcdcf8e3d68eec4a25fdd Code
MetaData_Integrating_lines_kalman.txt Feb 19, 2024 - Integrating VGI contributions for gully mapping using Kalman filter and machine learning Plain Text - 2.7 KB - MD5: 3592aa831001b5a26154a7ba6c3109fd
reference_left.tab Feb 19, 2024 - Integrating VGI contributions for gully mapping using Kalman filter and machine learning Tabular Data - 31.6 KB - 8 Variables, 463 Observations - UNF:6:dbfZe/C8CmWXcBEZJg2RPw==

An Automatic Iterative Random Forest approach to derive gully activity maps in large areas with training data scarcity [Data and Source Code]

May 13, 2024

Vallejo-Orti, Miguel; Winiwarter, Lukas; Corral, Eva; Williams, Jack; Bubenzer, Olaf; Höfle, Bernhard, 2024, "An Automatic Iterative Random Forest approach to derive gully activity maps in large areas with training data scarcity [Data and Source Code]", https://doi.org/10.11588/data/WGAU4Q, heiDATA, V1

Gullies are landforms with specific patterns of shape, topography, hydrology, vegetation, and soil characteristics. Remote sensing products (TanDEM-X, Sentinel-1, and Sentinel-2) serve as inputs into an iterative algorithm, initialized using a micro-mapping simulation as training...

Codes.zip

May 13, 2024 - An Automatic Iterative Random Forest approach to derive gully activity maps in large areas with training data scarcity [Data and Source Code]

ZIP Archive - 12.9 KB -

Code

Inputs.zip

May 13, 2024 - An Automatic Iterative Random Forest approach to derive gully activity maps in large areas with training data scarcity [Data and Source Code]

Shapefile as ZIP Archive - 7.1 KB -

Data

MetaData_iterative_random_forest.txt

May 13, 2024 - An Automatic Iterative Random Forest approach to derive gully activity maps in large areas with training data scarcity [Data and Source Code]

Plain Text - 5.9 KB -

Codes metadata

Outputs.zip

May 13, 2024 - An Automatic Iterative Random Forest approach to derive gully activity maps in large areas with training data scarcity [Data and Source Code]

Shapefile as ZIP Archive - 876.5 KB -

Data

Integrating VGI contributions for gully mapping using Kalman filter and machine learning

Feb 19, 2024

Vallejo Orti, Miguel; Anders, Katharina; Ajali, Oliubikum; Bubenzer, Olaf; Höfle, Bernhard, 2024, "Integrating VGI contributions for gully mapping using Kalman filter and machine learning", https://doi.org/10.11588/data/UHSQG0, heiDATA, V1, UNF:6:dbfZe/C8CmWXcBEZJg2RPw== [fileUNF]

The codes and datsets included are related to experiments and results conducted to integrate different lines digitized by volunteers using Kalman filter with changing amount of input lines. Three approaches are included: i) Kalman filtering integration to investigate the role of...

Cross_Training_Integration.zip

Feb 19, 2024 - Integrating VGI contributions for gully mapping using Kalman filter and machine learning

ZIP Archive - 1.9 MB -

Kalman_filter_integration.py

Feb 19, 2024 - Integrating VGI contributions for gully mapping using Kalman filter and machine learning

Python Source Code - 23.7 KB -

Code

MetaData_Integrating_lines_kalman.txt

Feb 19, 2024 - Integrating VGI contributions for gully mapping using Kalman filter and machine learning

Plain Text - 2.7 KB -

reference_left.tab

Feb 19, 2024 - Integrating VGI contributions for gully mapping using Kalman filter and machine learning

Tabular Data - 31.6 KB - 8 Variables, 463 Observations -

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