Classification of Types of Changes in Gully Environments Using Time Series Forest Algorithm [data] (doi:10.11588/data/NSMM6P)

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Part 1: Document Description
Part 2: Study Description
Part 3: Data Files Description
Part 4: Variable Description
Part 5: Other Study-Related Materials
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Classification of Types of Changes in Gully Environments Using Time Series Forest Algorithm [data]

doi:10.11588/data/NSMM6P

heiDATA

2023-07-24

2

Vallejo Orti, Miguel; Castillo, Carlos; Zahs, Vivien; Bubenzer, Olaf; Höfle, Bernhard, 2023, "Classification of Types of Changes in Gully Environments Using Time Series Forest Algorithm [data]", https://doi.org/10.11588/data/NSMM6P, heiDATA, V2, UNF:6:KVUhApCn+Ker99oncknXzA== [fileUNF]

Classification of Types of Changes in Gully Environments Using Time Series Forest Algorithm [data]

doi:10.11588/data/NSMM6P

Vallejo Orti, Miguel (Institute of Geography, Heidelberg University, Germany)

Castillo, Carlos (Department of Rural Engineering, Civil Constructions and Engineering Projects, University of Córdoba)

Zahs, Vivien (3D Geospatial Data Processing Group, Institute of Geography, Heidelberg University, Germany)

Bubenzer, Olaf (Institute of Geography, Heidelberg University, Germany)

Höfle, Bernhard (3D Geospatial Data Processing Group, Institute of Geography, Heidelberg University, Germany)

heiDATA

Vallejo Orti, Miguel

https://doi.org/10.11588/data/NSMM6P

Earth and Environmental Sciences, Gully change, time series, SAR, Sentinel 1

This code implements the TimeSeriesForest algorithm to classify different types of changes in gully environments. i)gully topographical change, ii)no change outside gully, iii) no change inside gully, and iv) non-topographical change. The algorithm is specifically designed for time series classification tasks, where the input data represents the characteristics of gullies over time. The code follows a series of steps to prepare the data, train the classifier, calculate performance metrics, and generate predictions. The data preparation phase involves importing training and testing data from CSV files. The training data is then divided into classes based on their labels, and a subset of the top rows is selected for each class to create a balanced training dataset. Time series data and corresponding labels are extracted from the training data, while only the time series data is extracted from the testing data. Next, the code calculates various performance metrics to evaluate the trained classifier. It splits the training data into training and testing sets, initializes the TimeSeriesForest classifier, and trains it using the training set. The accuracy of the classifier is calculated on the testing set, and feature importances are determined. Predictions are generated for both the testing set and new data using the trained classifier. The code then computes a confusion matrix to analyze the classification results, visualizing it using Seaborn and Matplotlib. Performance metrics such as True Accuracy, Kappa, Producer's Accuracy, and User's Accuracy are calculated and printed to assess the classifier's effectiveness in classifying gully changes. Lastly, the code performs ensemble predictions by combining the testing data with the generated predictions. The results, including predictions and associated probabilities, are saved to an output file. Overall, this code provides a practical implementation of the TimeSeriesForest algorithm for classifying types of changes in gully environments, demonstrating its potential for environmental monitoring and management.

Vallejo Orti, M., Castillo, C., Zahs, V., Bubenzer, O. & Höfle, B. (2024): Classifying types of gully changes with unoccupied aircraft vehicles 3D multitemporal point clouds for training of satellite data analysis in Northwest Namibia. Earth Surface Processes and Landforms. Vol. Early View, pp. 1-21.

10.1002/esp.5759

Vallejo Orti, M., Castillo, C., Zahs, V., Bubenzer, O. & Höfle, B. (2024): Classifying types of gully changes with unoccupied aircraft vehicles 3D multitemporal point clouds for training of satellite data analysis in Northwest Namibia. Earth Surface Processes and Landforms. Vol. Early View, pp. 1-21.

• Number of cases: 12422

• No. of variables per record: 55

• Type of File: text/tab-separated-values

UNF:6:KVUhApCn+Ker99oncknXzA==

• X - X
• Y - Y
• Predictions - Predictions
• Predictions2 - Predictions2
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Variable Format: numeric

Notes: UNF:6:XOQ3u864+sESXjizRGlqwQ==

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Variable Format: numeric

Notes: UNF:6:wWEJKpu9iARb/2Fg5NSuug==

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Variable Format: character

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Variable Format: numeric

Notes: UNF:6:dKovhvJcoYL6TLPkJynrLw==

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Summary Statistics: Max. 0.074803305; Valid 12422.0; Mean 0.010794625256238935; StDev 0.007437589814294935; Min. 0.001172006;

Variable Format: numeric

Notes: UNF:6:hyHmk18WNmuAwy5ZdgamqQ==

f12484 Location:

Summary Statistics: Valid 12422.0; Min. 0.001334117; StDev 0.007570218008591655; Max. 0.079330642; Mean 0.010787936722347452

Variable Format: numeric

Notes: UNF:6:VXt7qRAXIbVH95Rh1uRtoA==

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Summary Statistics: Min. 0.001063774; StDev 0.007505309490176519; Valid 12422.0; Max. 0.077866973; Mean 0.011062820771051363

Variable Format: numeric

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Summary Statistics: Mean 0.011846274694976653; Valid 12422.0; StDev 0.00844324244185594; Min. 0.001029943; Max. 0.086299033

Variable Format: numeric

Notes: UNF:6:n1E+Ic/act95Uj6uQHc4BQ==

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Summary Statistics: Max. 0.074225323; StDev 0.007856379121304956; Valid 12422.0; Min. 0.001491126; Mean 0.01137166848309451

Variable Format: numeric

Notes: UNF:6:u4msAoewGIFUzcNGKWJoOQ==

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Summary Statistics: Valid 12422.0; StDev 0.008152654356125204; Mean 0.011702363583883428; Min. 0.001284168; Max. 0.096952108

Variable Format: numeric

Notes: UNF:6:I4sPrKp+RpBmft4hQ8IDMw==

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Summary Statistics: Max. 0.075710522; Valid 12422.0; StDev 0.008360765435721123; Mean 0.01187562698510707; Min. 0.001334837

Variable Format: numeric

Notes: UNF:6:6O8TS9dQtkndPqnbW2Iusw==

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Summary Statistics: Min. 0.001217736; Mean 0.013883580434310096; Valid 12422.0; StDev 0.009327925723583952; Max. 0.087832522;

Variable Format: numeric

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Variable Format: numeric

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Variable Format: numeric

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Summary Statistics: Min. 9.74965E-4; Valid 12422.0; Max. 0.058802038; StDev 0.006875042432627503; Mean 0.010146455586298505;

Variable Format: numeric

Notes: UNF:6:akVeupxzhRHjglRRjtScKQ==

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Summary Statistics: StDev 0.007047108674875724; Min. 0.001202336; Valid 12422.0; Max. 0.070776626; Mean 0.009892150876831424;

Variable Format: numeric

Notes: UNF:6:9VWSHtj2HmGN2FycKh8V5Q==

f12484 Location:

Summary Statistics: Max. 0.056427622; Valid 12422.0; StDev 0.006803982621158645; Mean 0.010376146654644983; Min. 9.87865E-4

Variable Format: numeric

Notes: UNF:6:caTor4X/IVvCLMor/j54ZQ==

f12484 Location:

Summary Statistics: Max. 0.073171763; Min. 0.001383149; Valid 12422.0; StDev 0.006892060746362689; Mean 0.010078117707293511

Variable Format: numeric

Notes: UNF:6:VJZM+R1N/bh3EM0JcUvGuQ==

f12484 Location:

Summary Statistics: Mean 0.01034165963073579; Max. 0.067126896; Min. 0.001153715; StDev 0.007246063385910825; Valid 12422.0

Variable Format: numeric

Notes: UNF:6:m2a6UBv153ZdFbzfW4cQHg==

f12484 Location:

Summary Statistics: Valid 12422.0; Min. 0.001160779; Max. 0.052976814; Mean 0.009909627188053452; StDev 0.006311177916252428

Variable Format: numeric

Notes: UNF:6:S02KZSNioW8MQLdgS+6nfA==

f12484 Location:

Summary Statistics: Max. 0.070087233; Valid 12422.0; Min. 0.00135019; StDev 0.006619550437033074; Mean 0.009972066689180487

Variable Format: numeric

Notes: UNF:6:hV8sFHCv9sB5MX6w8x13IQ==

f12484 Location:

Summary Statistics: Valid 12422.0; StDev 0.006785041674024357; Mean 0.01036207904306875; Max. 0.064167537; Min. 0.001281681

Variable Format: numeric

Notes: UNF:6:5jnaaqKNusYR1FFRh1cn9g==

f12484 Location:

Summary Statistics: StDev 0.0069831026409167395; Max. 0.06782119; Min. 0.001473068; Mean 0.010764457257204957; Valid 12422.0

Variable Format: numeric

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f12484 Location:

Summary Statistics: Min. 0.001085381; Mean 0.010557466457092254; Valid 12422.0; Max. 0.057611734; StDev 0.006728903911548635;

Variable Format: numeric

Notes: UNF:6:ZwI8u6FH8w0WitjgCEabDw==

f12484 Location:

Summary Statistics: Valid 12422.0; StDev 0.006937467913713071; Mean 0.010483186237642893; Min. 0.001131548; Max. 0.064775421

Variable Format: numeric

Notes: UNF:6:05UfP2iGIDBp9OW7ouhOdw==

f12484 Location:

Summary Statistics: Valid 12422.0; StDev 0.009359354577453799; Mean 0.013858541390597327; Max. 0.093137984; Min. 0.001679321

Variable Format: numeric

Notes: UNF:6:D4vMEK9IjUyxRXP5o3XQuw==

f12484 Location:

Summary Statistics: Min. 0.00112123; StDev 0.006778959833565988; Valid 12422.0; Max. 0.068515518; Mean 0.010473219398003542

Variable Format: numeric

Notes: UNF:6:DdL66iP8sh/bty/TMsQECA==

f12484 Location:

Summary Statistics: Mean 0.010523909966833039; Max. 0.06503759; Min. 0.0013226; Valid 12422.0; StDev 0.006859371474914462

Variable Format: numeric

Notes: UNF:6:TuzNXNoz2jBF4FO8duyzzQ==

f12484 Location:

Summary Statistics: Valid 12422.0; Max. 0.053189358; StDev 0.00646015265173581; Mean 0.0099801960379971; Min. 6.60247E-4;

Variable Format: numeric

Notes: UNF:6:CDenCOKbWlTb+Wsan5NKDg==

f12484 Location:

Summary Statistics: Mean 0.01031002980969248; Valid 12422.0; Max. 0.058366834; StDev 0.006853460066721621; Min. 0.001299177;

Variable Format: numeric

Notes: UNF:6:gCRTtZdDRFYwB4i+9wKguw==

f12484 Location:

Summary Statistics: Mean 0.010215768829898565; Max. 0.078180915; Min. 0.001058169; StDev 0.006800348404979559; Valid 12422.0

Variable Format: numeric

Notes: UNF:6:IlTmFptCwSBFsfzxSYY3Ig==

f12484 Location:

Summary Statistics: StDev 0.006796200263052156; Min. 9.8809E-4; Max. 0.065801387; Valid 12422.0; Mean 0.01026618208026083

Variable Format: numeric

Notes: UNF:6:2aZgGYpJV2rBIWS+qDrqhQ==

f12484 Location:

Summary Statistics: Valid 12422.0; Mean 0.010338293474400257; StDev 0.006915180547541135; Max. 0.067549513; Min. 9.17407E-4

Variable Format: numeric

Notes: UNF:6:SgA1Cn5ZOCPsx2P6tlin+g==

f12484 Location:

Summary Statistics: Mean 0.010447416626308163; StDev 0.006954066722118989; Max. 0.063920067; Valid 12422.0; Min. 0.001329637;

Variable Format: numeric

Notes: UNF:6:eO7el6EmA5qX17gJZeHhbw==

f12484 Location:

Summary Statistics: Min. 0.001244895; Mean 0.010979228364353564; StDev 0.007744251718773705; Max. 0.067787198; Valid 12422.0

Variable Format: numeric

Notes: UNF:6:D4J+Xlh+buj8kmrMXv3OEw==

f12484 Location:

Summary Statistics: Max. 0.060863121; Min. 9.89484E-4; StDev 0.007455474329499179; Mean 0.01086228214361617; Valid 12422.0;

Variable Format: numeric

Notes: UNF:6:9F0esMzOhwwhfv7Dny7k/w==

f12484 Location:

Summary Statistics: StDev 0.008336617888724846; Max. 0.06712534; Min. 0.001093161; Valid 12422.0; Mean 0.011409422101110931;

Variable Format: numeric

Notes: UNF:6:d0dBsc+ktx8m9JW4Rt8ooQ==

f12484 Location:

Summary Statistics: Valid 12422.0; Max. 0.094462576; StDev 0.008381401072087973; Mean 0.011417117033971986; Min. 9.35412E-4

Variable Format: numeric

Notes: UNF:6:DQ8QQOEwfiofdXrUq2feSg==

f12484 Location:

Summary Statistics: Valid 12422.0; Max. 0.080012519; Min. 0.001143967; Mean 0.011752230464981486; StDev 0.008641158404864962

Variable Format: numeric

Notes: UNF:6:3nkYvzrfM2G3Sg9y2L7qfA==

f12484 Location:

Summary Statistics: Mean 0.011708843440186767; Min. 0.001249542; Valid 12422.0; StDev 0.008361653540677833; Max. 0.066240891;

Variable Format: numeric

Notes: UNF:6:isKYuoDZc3xFnQxIstu7fw==

f12484 Location:

Summary Statistics: Valid 12422.0; Min. 0.001120463; Max. 0.097087634; Mean 0.01147530986588311; StDev 0.008535973992701196

Variable Format: numeric

Notes: UNF:6:GWHP036P4nLG7IbWnmAL1g==

f12484 Location:

Summary Statistics: Max. 0.080965368; StDev 0.0077474968125653436; Valid 12422.0; Mean 0.010881779140074063; Min. 6.44899E-4

Variable Format: numeric

Notes: UNF:6:qV0jVROBVIY8fTZe5R3tLA==

f12484 Location:

Summary Statistics: Min. 0.001065983; Mean 0.01047169681073901; Valid 12422.0; Max. 0.079490632; StDev 0.007605962525571054;

Variable Format: numeric

Notes: UNF:6:7yWe18SvUCuZVfRnkkHDrQ==

f12484 Location:

Summary Statistics: Min. 0.001047371; StDev 0.007621001644326746; Mean 0.010558236039768154; Valid 12422.0; Max. 0.077923948

Variable Format: numeric

Notes: UNF:6:ha+BsBd8zbKiWigKLfrbsw==

f12484 Location:

Summary Statistics: Min. 0.001249909; Valid 12422.0; Max. 0.06114713; Mean 0.010422522841490905; StDev 0.007049334371326052

Variable Format: numeric

Notes: UNF:6:IW0MePTUo1xOq6VyqGpDuA==

f12484 Location:

Summary Statistics: Mean 0.010533273423361775; Valid 12422.0; Max. 0.085927522; Min. 0.00110041; StDev 0.007534449143458973

Variable Format: numeric

Notes: UNF:6:aZ5mj1MuWoYGVoeDJ9hTEw==

f12484 Location:

Summary Statistics: Mean 0.010096787746176143; Min. 9.28175E-4; Valid 12422.0; Max. 0.058659442; StDev 0.007331078737315687

Variable Format: numeric

Notes: UNF:6:e/LR2x3pA/k7xhEX+NuLSA==

f12484 Location:

Summary Statistics: Mean 0.009496826560054742; StDev 0.006817532275185895; Min. 9.70368E-4; Valid 12422.0; Max. 0.070009636

Variable Format: numeric

Notes: UNF:6:L46QmmbRmB22Y2YUKUMckg==

f12484 Location:

Summary Statistics: Min. 0.001061856; Valid 12422.0; StDev 0.006654954514188562; Mean 0.009591528658831108; Max. 0.05675151

Variable Format: numeric

Notes: UNF:6:3ao6wqDdN+w3aQQ9vGPFrg==

f12484 Location:

Summary Statistics: Valid 12422.0; Min. 0.001031535; StDev 0.00671792371085058; Mean 0.009887442157704077; Max. 0.079699011

Variable Format: numeric

Notes: UNF:6:UIRddBIRC/KHs0II3uWlLg==

f12484 Location:

Summary Statistics: Mean 0.011378814820238285; Min. 0.001162916; Max. 0.08612804; Valid 12422.0; StDev 0.008133625755530128

Variable Format: numeric

Notes: UNF:6:xcouGPGZNB4M/ABl8NtzBw==

f12484 Location:

Summary Statistics: StDev 0.006635686495898435; Mean 0.010269588647399776; Valid 12422.0; Min. 9.56029E-4; Max. 0.074910581;

Variable Format: numeric

Notes: UNF:6:lStUDLVo/sUiiz2Hz/hOCA==

f12484 Location:

Summary Statistics: Min. 0.001214633; Valid 12422.0; StDev 0.006672565502787915; Max. 0.058514369; Mean 0.010239794656094026

Variable Format: numeric

Notes: UNF:6:3D+Xkqm22UtjdYX/jd/z9Q==

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Metadata_TSF.txt

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Prediction_VH_Kunene-1.csv

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Prediction_VH_Kunene.csv

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Raster_Operations_Clean_Code.R.txt

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target_very_big_Kunene_VH.csv

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time_series_forest.py

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training_VH_Kunene.csv

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