analysis/scripts/11_workflows.R
In keltoskytoi/iSEGMound: iSEGMound - A Reproducible Workflow for Mound Detection in LiDAR-derived DTMs
#flowchart of Random Forest Algorithm
mermaid("graph TB
A((dataset))-->B[Decision Tree 1]
A((dataset))-->C[Decision Tree 2]
A((dataset))-->D[Decision Tree n]
B[Decision Tree 1]-.->|Prediction 1|E[majority voting/averaging]
C[Decision Tree 2]-.->|Prediction 2|E[majority voting/averaging]
D[Decision Tree n]-.->|Prediction n|E[majority voting/averaging]
E[majority voting/averaging]-->F[final prediction]
style A fill:#FFF, stroke:#333, stroke-width:3px
style B fill:#5F9EA0, stroke:#2F4F4F, stroke-width:3px
style C fill:#5F9EA0, stroke:#2F4F4F, stroke-width:3px
style D fill:#5F9EA0, stroke:#2F4F4F, stroke-width:3px
style E fill:#d5f4e6, stroke:#618685, stroke-width:2px
style F fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5")
#flowchart of Automated Analysis Methods
DiagrammeR("graph TB
A(Automated Analysis in Archaeological Remote Sensing)==>B[Template Matching-based]
A(Automated Analysis in Archaeological Remote Sensing)==>C[Geometric knowledge-based]
A(Automated Analysis in Archaeological Remote Sensing)==>D[GeOBIA-based]
A(Automated Analysis in Archaeological Remote Sensing)==>E[Machine Learning-based]
B[Template Matching-based]-.->F(Rigid Template Matching)
B[Template Matching-based]-.->G(Deformable Template Matching)
C[Geometric knowledge-based]-.->H(Geometric Knowledge)
C[Geometric knowledge-based]-.->I(Context Knowledge)
D[GeOBIA-based]-.->J(Image Segmentation)
J(Image Segmentation)-.->K(Object Classification)
E[Machine Learning-based]-.->L(Feature Extraction)
L(Feature Extraction)-.->M(Feature fusion)
M(Feature fusion)-.->N(Classifier training)
E[Machine Learning-based]-->O[Deep Learning]
O[Deep Learning]-.->P(Network Architecture)
P(Network Architecture)-.->Q(Classifier)
style A fill:#d5f4e6, stroke:#618685, stroke-width:5px
style B fill:#d5f4e6, stroke:#618685, stroke-width:2px
style C fill:#d5f4e6, stroke:#618685, stroke-width:2px
style D fill:#d5f4e6, stroke:#618685, stroke-width:2px
style E fill:#d5f4e6, stroke:#618685, stroke-width:2px
style F fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style G fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style H fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style I fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style J fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style K fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style L fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style M fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style N fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style O fill:#d5f4e6, stroke:#618685, stroke-width:2px
style P fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style Q fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5")
#or
DiagrammeR("graph LR
A[DTM/MSTPI]->>B[Template Matching-based]
B[Template Matching-based]-.->F(Rigid Template Matching)
B[Template Matching-based]-.->G(Deformable Template Matching)
C[Geometric knowledge-based]-.->H(Geometric Knowledge)
C[Geometric knowledge-based]-.->I(Context Knowledge)
D[GeOBIA-based]-.->J(Image Segmentation)
J(Image Segmentation)-.->K(Object Classification)
E[Machine Learning-based]-.->L(Feature Extraction)
L(Feature Extraction)-.->M(Feature fusion)
M(Feature fusion)-.->N(Classifier training)
E[Machine Learning-based]-->O[Deep Learning]
O[Deep Learning]-.->P(Network Architecture)
P(Network Architecture)-.->Q(Classifier)
style A fill:#d5f4e6, stroke:#618685, stroke-width:5px
style B fill:#d5f4e6, stroke:#618685, stroke-width:2px
style C fill:#d5f4e6, stroke:#618685, stroke-width:2px
style D fill:#d5f4e6, stroke:#618685, stroke-width:2px
style E fill:#d5f4e6, stroke:#618685, stroke-width:2px
style F fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style G fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style H fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style I fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style J fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style K fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style L fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style M fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style N fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style O fill:#d5f4e6, stroke:#618685, stroke-width:2px
style P fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style Q fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5")
#general automated analysis workflow
mermaid("
sequenceDiagram
i Data PreProcessing->>ii Data Preparation: LAS/LAZ, .xyz file
alt choose Data Preparation method/s
ii Data Preparation->>iii Data Analysis: ii-i visualisation methods
ii Data Preparation->>iii Data Analysis: ii-ii textural analysis
ii Data Preparation->>iii Data Analysis: ii-iii morphometric analysis
ii Data Preparation->>iii Data Analysis: ii-iv morphometric enhancement
ii Data Preparation->>iii Data Analysis: ii-v morphometric extraction
else choose Data Analysis method/s
iii Data Analysis->>iv Data interpretation/verification: iii-i Geometric-knowledge-based
iii Data Analysis->>iv Data interpretation/verification: iii-ii GeOBIA
iii Data Analysis->>iv Data interpretation/verification: iii-iii Machine Learning
end
")
#workflow DTM generation
mermaid("
sequenceDiagram
LAS/LAZ files ->>Ground Point Re/Classification: lidR::readLAS/readLAScatalog
Note right of LAS/LAZ files: select attributes, filter class, lidR::lascheck
alt choose Point Cloud Re/Classification Algorithm
LAS/LAZ files->>Spatial Interpolation: A) Ground Classification (select = xyzirnc + filter = keep_class_2)
Note right of Ground Point Re/Classification: lidR:: classify_ground
LAS/LAZ files ->>Ground Point Re/Classification: select = xyzirnc
Ground Point Re/Classification->>Spatial Interpolation: B) Progressive Morphological Filter (PMF)
Ground Point Re/Classification->>Spatial Interpolation: C) Cloth Simulation Function (CSF)
else choose Spatial Interpolation Algorithm
Note right of Spatial Interpolation: lidR:: grid_terrain
Spatial Interpolation->> DTM:Triangulated Irregular Networks (TIN)
Spatial Interpolation->> DTM:Inverse Distance Weighing (IDW)
end
")
#Workflow Freeland et al 2016
mermaid("
sequenceDiagram
DTM->>fDTM: mean filter
fDTM->>ifDTM: invert raster
ifDTM->>pf ifDTM: fill pits
Note right of ifDTM: Wang & Liu 2006
pf ifDTM->>diff ifDTM: ifDTM-pf ifDTM
Note left of possible earthworks: optional filtering of diff ifDTM before morphometric rules
diff ifDTM->> possible earthworks: morphometric rules
")
#Workflow Davis et al 2019
mermaid("
sequenceDiagram
DTM->>iDTM: invert raster
Note left of deprMaps: Monte Carlo Simulation to map depressions
iDTM->>deprMaps: SDA Whitebox GAT - comparision of results
deprMaps->>fdeprMaps: filter to size
fdeprMaps->>possible mound shellrings: comparsion to lu maps
")
#Workflow Rom et al 2020
mermaid("
sequenceDiagram
DTM->>fDTM: lowpass filter
fDTM->>ifDTM: invert raster
ifDTM->>pf ifDTM: fill pits
Note right of ifDTM: Wang & Liu 2006
pf ifDTM->>diff ifDTM: ifDTM-pf ifDTM
diff ifDTM->> thresh diff ifDTM: thresholds
Note right of diff ifDTM: min height, max area
thresh diff ifDTM->>possible tell mounds: invert raster
")
################################################################################
###-------------------#iSEGMound: Different workflow versions#--------------####
################################################################################
#workflow thesis - scheme
mermaid("graph TB
A[LiDAR las/laz files]-->|1 poss. morphometric extraction|B[DTM/MSTPI]
A[LiDAR las/laz files]-->|0 data preprocessing|A[LiDAR las/laz files]
B[DTM/MSTPI]-->|2 iMound|C[filt diff ifDTM/ifdDTM]
C[filt diff ifDTM/ifdDTM]-->|3 segmentation|D[segments with descriptors]
D[segments with descriptors]-->|4 thresholding|E[possible mound segments]
E[possible mound segments]-->|5 verification|F[detected burial mounds]
style A fill:#FFF, stroke:#333, stroke-width:px
style B fill:#F5DEB3, stroke:#8B7E66, stroke-width:2px
style C fill:#DC8A3B, stroke:#564109, stroke-width:2px
style D fill:#B6AE4B, stroke:#626610, stroke-width:2px
style E fill:#8DA49C, stroke:#43595E, stroke-width:2px
style F fill:#AEA594, stroke:#1C1A09, stroke-width:2px, stroke-dasharray: 5 5")
#Workflow of the thesis - simple
mermaid("
sequenceDiagram
DTM/MTPI->>fDTM/fdDTM: filter
Note right of DTM/MTPI: raster::focal, fun=mean
fDTM/fdDTM->>ifDTM/ifdDTM: invert raster
Note right of fDTM/fdDTM: spatialEco::raster.invert
ifDTM/ifdDTM->>pf ifDTM/ifdDTM: Wang & Liu 2006 fill pits
Note right of ifDTM/ifdDTM: RSAGA:: ta_preprocessor 4
pf ifDTM/ifdDTM->>diff ifDTM/ifdDTM: ifDTM - pf ifDTM
diff ifDTM/ifdDTM->> filt diff ifDTM/ifdDTM: filter
Note right of diff ifDTM/ifdDTM: raster::focal, fun=?
filt diff ifDTM/ifdDTM->> Segmentation WS/RG: segment
Note right of filt diff ifDTM/ifdDTM: RSAGA:: imagery_segm 0/2+3
Segmentation WS/RG->>Segments: polygonize
Note right of Segments: RSAGA:: shapes_grid 6
Segments->>Joined Segments: join neighbors
Joined Segments->>Segments SHI: compute Shape Index
Note right of Joined Segments: RSAGA:: shapes_polygons 7
Segments SHI->>Segment Descriptors: calculate variables
Note right of Segments SHI: Niculită 2020
Segment Descriptors->>possible barrows: thresholds
")
#more complex
mermaid("
sequenceDiagram
alt choose Data Preparation Method
DTM->>dDTM: MTPI
Note left of dDTM: RSAGA:: ta_morphometry 28
DTM->>dDTM: STEP 1 POSS. MORPHOMETRY
dDTM->>fDTM/fdDTM: low-pass/focal filter
Note right of dDTM: raster::focal, fun=mean
DTM->>fDTM/fdDTM: low-pass/focal filter
Note right of dDTM: raster::focal, fun=mean
fDTM/fdDTM->>ifDTM/ifdDTM: invert raster
Note right of fDTM/fdDTM: spatialEco::raster.invert
ifDTM/ifdDTM->>pf ifDTM/ifdDTM: Wang & Liu 2006 fill pits
Note right of ifDTM/ifdDTM: RSAGA:: ta_preprocessor 4
pf ifDTM/ifdDTM->>diff ifDTM/ifdDTM: ifDTM - pf ifDTM
dDTM->>diff ifDTM/ifdDTM: STEP 2 iMound
diff ifDTM/ifdDTM->> filt diff ifDTM/ifdDTM: choose adapt filter
Note right of diff ifDTM/ifdDTM: raster::focal, fun=?
else choose Segmentation Algorithm
filt diff ifDTM/ifdDTM->> Segmentation: Watershed
Note right of filt diff ifDTM/ifdDTM: RSAGA:: imagery_segm 0
filt diff ifDTM/ifdDTM->> Segmentation: Region Growing
Note right of filt diff ifDTM/ifdDTM: RSAGA:: imagery_segm 2+3
Segmentation->>Segments: polygonize
Note left of Segments: RSAGA:: shapes_grid 6
Segments->>Joined Segments: join neighbors
Note right of Segments: custom R function
Joined Segments->>Segments & SI: compute Shape Index
Note right of Joined Segments: RSAGA:: shapes_polygons 7
Segments & SI->>Segment Descriptors: calculate Descriptors
diff ifDTM/ifdDTM->>Segment Descriptors: STEP 3 SEGMENTATION
Note right of Segments & SI: Niculită 2020
Segment Descriptors->>possible mound segments: STEP 4: thresholding
Note left of possible mound segments: of Index & Descriptor values
possible mound segments->>detected burial mounds: STEP 5: VERIFICATION
end
")
keltoskytoi/iSEGMound documentation built on Dec. 21, 2021, 5:24 a.m.
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#flowchart of Random Forest Algorithm
mermaid("graph TB
A((dataset))-->B[Decision Tree 1]
A((dataset))-->C[Decision Tree 2]
A((dataset))-->D[Decision Tree n]
B[Decision Tree 1]-.->|Prediction 1|E[majority voting/averaging]
C[Decision Tree 2]-.->|Prediction 2|E[majority voting/averaging]
D[Decision Tree n]-.->|Prediction n|E[majority voting/averaging]
E[majority voting/averaging]-->F[final prediction]
style A fill:#FFF, stroke:#333, stroke-width:3px
style B fill:#5F9EA0, stroke:#2F4F4F, stroke-width:3px
style C fill:#5F9EA0, stroke:#2F4F4F, stroke-width:3px
style D fill:#5F9EA0, stroke:#2F4F4F, stroke-width:3px
style E fill:#d5f4e6, stroke:#618685, stroke-width:2px
style F fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5")
#flowchart of Automated Analysis Methods
DiagrammeR("graph TB
A(Automated Analysis in Archaeological Remote Sensing)==>B[Template Matching-based]
A(Automated Analysis in Archaeological Remote Sensing)==>C[Geometric knowledge-based]
A(Automated Analysis in Archaeological Remote Sensing)==>D[GeOBIA-based]
A(Automated Analysis in Archaeological Remote Sensing)==>E[Machine Learning-based]
B[Template Matching-based]-.->F(Rigid Template Matching)
B[Template Matching-based]-.->G(Deformable Template Matching)
C[Geometric knowledge-based]-.->H(Geometric Knowledge)
C[Geometric knowledge-based]-.->I(Context Knowledge)
D[GeOBIA-based]-.->J(Image Segmentation)
J(Image Segmentation)-.->K(Object Classification)
E[Machine Learning-based]-.->L(Feature Extraction)
L(Feature Extraction)-.->M(Feature fusion)
M(Feature fusion)-.->N(Classifier training)
E[Machine Learning-based]-->O[Deep Learning]
O[Deep Learning]-.->P(Network Architecture)
P(Network Architecture)-.->Q(Classifier)
style A fill:#d5f4e6, stroke:#618685, stroke-width:5px
style B fill:#d5f4e6, stroke:#618685, stroke-width:2px
style C fill:#d5f4e6, stroke:#618685, stroke-width:2px
style D fill:#d5f4e6, stroke:#618685, stroke-width:2px
style E fill:#d5f4e6, stroke:#618685, stroke-width:2px
style F fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style G fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style H fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style I fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style J fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style K fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style L fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style M fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style N fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style O fill:#d5f4e6, stroke:#618685, stroke-width:2px
style P fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style Q fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5")
#or
DiagrammeR("graph LR
A[DTM/MSTPI]->>B[Template Matching-based]
B[Template Matching-based]-.->F(Rigid Template Matching)
B[Template Matching-based]-.->G(Deformable Template Matching)
C[Geometric knowledge-based]-.->H(Geometric Knowledge)
C[Geometric knowledge-based]-.->I(Context Knowledge)
D[GeOBIA-based]-.->J(Image Segmentation)
J(Image Segmentation)-.->K(Object Classification)
E[Machine Learning-based]-.->L(Feature Extraction)
L(Feature Extraction)-.->M(Feature fusion)
M(Feature fusion)-.->N(Classifier training)
E[Machine Learning-based]-->O[Deep Learning]
O[Deep Learning]-.->P(Network Architecture)
P(Network Architecture)-.->Q(Classifier)
style A fill:#d5f4e6, stroke:#618685, stroke-width:5px
style B fill:#d5f4e6, stroke:#618685, stroke-width:2px
style C fill:#d5f4e6, stroke:#618685, stroke-width:2px
style D fill:#d5f4e6, stroke:#618685, stroke-width:2px
style E fill:#d5f4e6, stroke:#618685, stroke-width:2px
style F fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style G fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style H fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style I fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style J fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style K fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style L fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style M fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style N fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style O fill:#d5f4e6, stroke:#618685, stroke-width:2px
style P fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5
style Q fill:#d5f4e6, stroke:#618685, stroke-width:2px, stroke-dasharray: 5 5")
#general automated analysis workflow
mermaid("
sequenceDiagram
i Data PreProcessing->>ii Data Preparation: LAS/LAZ, .xyz file
alt choose Data Preparation method/s
ii Data Preparation->>iii Data Analysis: ii-i visualisation methods
ii Data Preparation->>iii Data Analysis: ii-ii textural analysis
ii Data Preparation->>iii Data Analysis: ii-iii morphometric analysis
ii Data Preparation->>iii Data Analysis: ii-iv morphometric enhancement
ii Data Preparation->>iii Data Analysis: ii-v morphometric extraction
else choose Data Analysis method/s
iii Data Analysis->>iv Data interpretation/verification: iii-i Geometric-knowledge-based
iii Data Analysis->>iv Data interpretation/verification: iii-ii GeOBIA
iii Data Analysis->>iv Data interpretation/verification: iii-iii Machine Learning
end
")
#workflow DTM generation
mermaid("
sequenceDiagram
LAS/LAZ files ->>Ground Point Re/Classification: lidR::readLAS/readLAScatalog
Note right of LAS/LAZ files: select attributes, filter class, lidR::lascheck
alt choose Point Cloud Re/Classification Algorithm
LAS/LAZ files->>Spatial Interpolation: A) Ground Classification (select = xyzirnc + filter = keep_class_2)
Note right of Ground Point Re/Classification: lidR:: classify_ground
LAS/LAZ files ->>Ground Point Re/Classification: select = xyzirnc
Ground Point Re/Classification->>Spatial Interpolation: B) Progressive Morphological Filter (PMF)
Ground Point Re/Classification->>Spatial Interpolation: C) Cloth Simulation Function (CSF)
else choose Spatial Interpolation Algorithm
Note right of Spatial Interpolation: lidR:: grid_terrain
Spatial Interpolation->> DTM:Triangulated Irregular Networks (TIN)
Spatial Interpolation->> DTM:Inverse Distance Weighing (IDW)
end
")
#Workflow Freeland et al 2016
mermaid("
sequenceDiagram
DTM->>fDTM: mean filter
fDTM->>ifDTM: invert raster
ifDTM->>pf ifDTM: fill pits
Note right of ifDTM: Wang & Liu 2006
pf ifDTM->>diff ifDTM: ifDTM-pf ifDTM
Note left of possible earthworks: optional filtering of diff ifDTM before morphometric rules
diff ifDTM->> possible earthworks: morphometric rules
")
#Workflow Davis et al 2019
mermaid("
sequenceDiagram
DTM->>iDTM: invert raster
Note left of deprMaps: Monte Carlo Simulation to map depressions
iDTM->>deprMaps: SDA Whitebox GAT - comparision of results
deprMaps->>fdeprMaps: filter to size
fdeprMaps->>possible mound shellrings: comparsion to lu maps
")
#Workflow Rom et al 2020
mermaid("
sequenceDiagram
DTM->>fDTM: lowpass filter
fDTM->>ifDTM: invert raster
ifDTM->>pf ifDTM: fill pits
Note right of ifDTM: Wang & Liu 2006
pf ifDTM->>diff ifDTM: ifDTM-pf ifDTM
diff ifDTM->> thresh diff ifDTM: thresholds
Note right of diff ifDTM: min height, max area
thresh diff ifDTM->>possible tell mounds: invert raster
")
################################################################################
###-------------------#iSEGMound: Different workflow versions#--------------####
################################################################################
#workflow thesis - scheme
mermaid("graph TB
A[LiDAR las/laz files]-->|1 poss. morphometric extraction|B[DTM/MSTPI]
A[LiDAR las/laz files]-->|0 data preprocessing|A[LiDAR las/laz files]
B[DTM/MSTPI]-->|2 iMound|C[filt diff ifDTM/ifdDTM]
C[filt diff ifDTM/ifdDTM]-->|3 segmentation|D[segments with descriptors]
D[segments with descriptors]-->|4 thresholding|E[possible mound segments]
E[possible mound segments]-->|5 verification|F[detected burial mounds]
style A fill:#FFF, stroke:#333, stroke-width:px
style B fill:#F5DEB3, stroke:#8B7E66, stroke-width:2px
style C fill:#DC8A3B, stroke:#564109, stroke-width:2px
style D fill:#B6AE4B, stroke:#626610, stroke-width:2px
style E fill:#8DA49C, stroke:#43595E, stroke-width:2px
style F fill:#AEA594, stroke:#1C1A09, stroke-width:2px, stroke-dasharray: 5 5")
#Workflow of the thesis - simple
mermaid("
sequenceDiagram
DTM/MTPI->>fDTM/fdDTM: filter
Note right of DTM/MTPI: raster::focal, fun=mean
fDTM/fdDTM->>ifDTM/ifdDTM: invert raster
Note right of fDTM/fdDTM: spatialEco::raster.invert
ifDTM/ifdDTM->>pf ifDTM/ifdDTM: Wang & Liu 2006 fill pits
Note right of ifDTM/ifdDTM: RSAGA:: ta_preprocessor 4
pf ifDTM/ifdDTM->>diff ifDTM/ifdDTM: ifDTM - pf ifDTM
diff ifDTM/ifdDTM->> filt diff ifDTM/ifdDTM: filter
Note right of diff ifDTM/ifdDTM: raster::focal, fun=?
filt diff ifDTM/ifdDTM->> Segmentation WS/RG: segment
Note right of filt diff ifDTM/ifdDTM: RSAGA:: imagery_segm 0/2+3
Segmentation WS/RG->>Segments: polygonize
Note right of Segments: RSAGA:: shapes_grid 6
Segments->>Joined Segments: join neighbors
Joined Segments->>Segments SHI: compute Shape Index
Note right of Joined Segments: RSAGA:: shapes_polygons 7
Segments SHI->>Segment Descriptors: calculate variables
Note right of Segments SHI: Niculită 2020
Segment Descriptors->>possible barrows: thresholds
")
#more complex
mermaid("
sequenceDiagram
alt choose Data Preparation Method
DTM->>dDTM: MTPI
Note left of dDTM: RSAGA:: ta_morphometry 28
DTM->>dDTM: STEP 1 POSS. MORPHOMETRY
dDTM->>fDTM/fdDTM: low-pass/focal filter
Note right of dDTM: raster::focal, fun=mean
DTM->>fDTM/fdDTM: low-pass/focal filter
Note right of dDTM: raster::focal, fun=mean
fDTM/fdDTM->>ifDTM/ifdDTM: invert raster
Note right of fDTM/fdDTM: spatialEco::raster.invert
ifDTM/ifdDTM->>pf ifDTM/ifdDTM: Wang & Liu 2006 fill pits
Note right of ifDTM/ifdDTM: RSAGA:: ta_preprocessor 4
pf ifDTM/ifdDTM->>diff ifDTM/ifdDTM: ifDTM - pf ifDTM
dDTM->>diff ifDTM/ifdDTM: STEP 2 iMound
diff ifDTM/ifdDTM->> filt diff ifDTM/ifdDTM: choose adapt filter
Note right of diff ifDTM/ifdDTM: raster::focal, fun=?
else choose Segmentation Algorithm
filt diff ifDTM/ifdDTM->> Segmentation: Watershed
Note right of filt diff ifDTM/ifdDTM: RSAGA:: imagery_segm 0
filt diff ifDTM/ifdDTM->> Segmentation: Region Growing
Note right of filt diff ifDTM/ifdDTM: RSAGA:: imagery_segm 2+3
Segmentation->>Segments: polygonize
Note left of Segments: RSAGA:: shapes_grid 6
Segments->>Joined Segments: join neighbors
Note right of Segments: custom R function
Joined Segments->>Segments & SI: compute Shape Index
Note right of Joined Segments: RSAGA:: shapes_polygons 7
Segments & SI->>Segment Descriptors: calculate Descriptors
diff ifDTM/ifdDTM->>Segment Descriptors: STEP 3 SEGMENTATION
Note right of Segments & SI: Niculită 2020
Segment Descriptors->>possible mound segments: STEP 4: thresholding
Note left of possible mound segments: of Index & Descriptor values
possible mound segments->>detected burial mounds: STEP 5: VERIFICATION
end
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