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adding files, final ipynbs, and sav models #12

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adding files, final ipynbs, and sav models #12

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adding files, final ipynbs, and sav models
giosans Nov 19, 2020
376e705
Update README.md
giosans Nov 19, 2020
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Added a blank space
MH0202 Dec 21, 2020
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Merge pull request #2 from MH0202/master
giosans Dec 21, 2020
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6 changes: 2 additions & 4 deletions README.md
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Expand Up @@ -3,7 +3,7 @@

The landslide_detector is a tool developed to detect landslides from optical remotely sensed images using Object-Based Image Analysis (OBIA) and Machine Learning (Random Forest classifier).

I developed this tool to test the methodology proposed in [my master thesis](https://repository.tudelft.nl/islandora/object/uuid%3A52fe6b3b-ec0b-4cad-b51d-7798830688a4?collection=education) in Geomatics at Delft University of Technology. This implementation can be used to assist landslides experts/non-experts in detecting new landslides events and improve existing inventories.
This tool was developed to test the methodology proposed in [my master thesis](https://repository.tudelft.nl/islandora/object/uuid%3A52fe6b3b-ec0b-4cad-b51d-7798830688a4?collection=education) in Geomatics at Delft University of Technology. This implementation can be used to assist landslides experts/non-experts in detecting new landslides events and improve existing inventories.
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I agree with this change

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ok


This project was made in join collaboration [Delft University of Technology](https://www.tudelft.nl/en/) and [Deltares Research Institute](https://www.deltares.nl/en/).

Expand All @@ -14,7 +14,7 @@ The tool is built using open source software: [Google Earth Engine(GEE)](https:/

- [Pre-processing script](https://github.com/mhscience/landslides_detection/blob/master/pre_processingGEE/pre_processing_thesis_mh.js) developed for Google Earth Engine. The script obtains cloud-free images from optical satellite imagery (Sentinel-2), extracts spectral and topographic features from Sentinel-2 and global Digital Elevation Model (DEM), and computes new landslides diagnostic features at pixel level

- [Image segmentation program](https://github.com/mhscience/landslides_detection/tree/master/segmentation) developed in Python. Image segmentation is the first step towards the application of OBIA. It consists on the subdivision of an image into spatially continuous, disjoint, and relative homogeneous regions that refer to segments. This stage is implemented as a two-step approach: (a) an initial segmentation using a [k-means script](https://github.com/mhscience/landslides_detection/tree/master/segmentation/k_means_segmentation) (developed using [RSGISLib](https://www.rsgislib.org/)); (b) [merging algorithm script](https://github.com/mhscience/landslides_detection/tree/master/segmentation/merging_algorithm) using a region-growing implementation
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We cannot remove this because it is linked to the master thesis. Let me think about how can I update this properly for the current paper.

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If you like my previous suggestion, we can go ahead like that. Hear from you

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Yes, fine!

- [Image segmentation program](https://github.com/mhscience/landslides_detection/tree/master/segmentation) developed in Python. Image segmentation is the first step towards the application of OBIA. It consists on the subdivision of an image into spatially continuous, disjoint, and relative homogeneous regions that refer to segments. This stage is implemented with an initial segmentation using a [k-means script](https://github.com/mhscience/landslides_detection/tree/master/segmentation/k_means_segmentation) (developed using [RSGISLib](https://www.rsgislib.org/)).

- [Image classification script](https://github.com/mhscience/landslides_detection/tree/master/model) to detect the landslide segments. Once segments with features statistics are obtained from the Image segmentation step, the image is classified by assigning each segment to a class. The classification is conducted using supervised Machine Learning, specifically the Random Forest algorithm

Expand All @@ -29,8 +29,6 @@ Master in Geomatics @ Delft University of Technology
Contact: mhscience@gmail.com

#### Contributors
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I can not delete people that contributed to this repository during the master thesis but I will modify the Readme to add the paper's contributor,, therefore the contributor's section will highlight who worked on what.

  • Contributors
    -- Paper XYZ (date)
    -- Master Thesis (date)

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I see. I propose to make a new repository from scratch, on my account, in which I'll also add an href to this one.

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Ok Perfect!

Dr.ir. Mathias Lemmens @ Delft University of Technology
Dr.ir. Amin Askarinejad @ Delft University of Technology
Dr.ir. Faraz Tehrani @ Deltares Research Institute
Ir. Giorgio Santinelli @ Deltares Research Institute

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