Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

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==
Add Data

Sign up or log in to create a dataverse or add a dataset.

Sign Up Log In
Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.