TECHNIQUES TO ENHANCE THE DETECTION OF PATHS WITH LIDAR · PDF filetechniques to enhance the detection of paths with lidar . seminar ‘detection and modelling of ancient pathways’

TECHNIQUES TO ENHANCE THE DETECTION OF PATHS WITH LIDAR

SEMINAR ‘DETECTION AND MODELLING OF ANCIENT PATHWAYS’ AMSTERDAM, 27 JUNE 2016 PHILIP VERHAGEN

ŽIGA KOKALJ

LAURE NUNINGER

Vrije Universiteit Amsterdam

visualisation tools

(semi-)automated detection

application to pathway detection

improving detection, interpretation and comparison

OUTLINE

2 SEMINAR ‘DETECTION AND MODELLING OF ANCIENT PATHWAYS’ 27/06/2016


visual recognition and manual delineation of objects of archaeological interest > building on experiences in aerial photography reconnaissance > data processing techniques geared towards enhancement of visual recognition > time-consuming > problem of non-comparability between operators

based on morphometric characteristics > visual / statistical contrast > shape (in 2D and 3 D) > size

DETECTION AND VISUALISATION



standard GIS operations > histogram stretch > hillshade > 3D

VISUALISATION TOOLS


http://www.ahn.nl/pagina/apps-en-tools/viewer.html http://ahn2.pointclouds.nl/

http://www.ahn.nl/pagina/apps-en-tools/viewer.html

http://ahn2.pointclouds.nl/


trend removal technique > smoothing of elevation within a neighbourhood > subtracting true elevation from smoothed elevation better reveals microtopography


ADVANCED VISUALISATION: LOCAL RELIEF MODELS

Bofinger and Hesse 2011


determines size of visible sky > looking into different directions within a neighbourhood > only looking upward


ADVANCED VISUALISATION: SKYVIEW FACTOR

Kokalj et al. 2010


quantifies the degree of unobstructedness of a location > Yokoyama et al. 2002, Doneus 2013


ADVANCED VISUALISATION: OPENNESS

skyview factor

positive openness

negative openness


SMRF filter > Simple MoRphological Filter

creates DTM from point-cloud data

> filters data with user-defined slope and window-size

> enhances features of a particular size and vertical prominence


ADVANCED VISUALISATION: BONEMAPPING

Pingel et al. 2015


hillshade > best in flat terrain, but: features parallel to light source > PCA of hillshading in multiple directions

slope > works well in combination with hillshading

local relief models > for use in gently sloping terrain, use hillshading afterwards > can create some artifacts, smoothing can blur contrasts

skyview factor > works well with ‘noisy’ data and complex features > not the best in very flat terrain

openness > well suited for the visualization of long linear subtle features like road and paths (Vletter 2015)

bonemapping > good at retaining subtle features > too much smoothing may blur contrasts


PROS AND CONS OF VISUALISATIONS

normal hillshaded terrain

enhanced visualization

Maya sacbe

combination of HS, SVF, and openness

modern and ancient crossroads


mule tracks


hollow ways



http://www.arcland.eu/news/1487-lidar-toolbox-livt-10019 > import- and export to GIS > no manual

http://iaps.zrc-sazu.si/en/rvt#v > import- and export to GIS > extensive manual > nice PowerPoint with more explanation

http://tpingel.org/code/smrf/smrf.html > Matlab only > No manual


VISUALISATION PACKAGES

http://www.arcland.eu/news/1487-lidar-toolbox-livt-10019

http://iaps.zrc-sazu.si/en/rvt#v

http://tpingel.org/code/smrf/smrf.html


techniques in development > (statistical) methods well established in remote sensing > problem: definition and classification of objects of interest

successful examples mainly restricted to simple shapes > mounds, pits (Trier et al. 2015) > linear structures (Vletter 2015) > more complex: rectangular enclosures (Zingman et al. 2015)

‘automation’ in two steps > define shapes of interest in mathematical and statistical

terms (2D and 3D) > extract features conforming to these definitions as

(vectorized) objects

segmentation > first extract different shapes, then classify them > advantage: can be used on multispectral images


AUTOMATED DETECTION

Zingman et al. 2015


remote sensing software is expensive > most users rely on packages like ENVI and ERDAS > open source options are limited (SAGA, GRASS)

segmentation software is even more expensive > eCognition is the most versatile > other packages only have limited options

successful case studies use custom-made tools > basically, we don’t know how well the tools perform


AUTOMATED DETECTION AND SOFTWARE

Vletter 2015


detection provides shapes, not archaeology > ground-truthing

> using other cartographic and written sources > fieldwork

automated detection is much quicker, but needs post-processing

> ‘undistinctive’ structures are harder to define in morphometric terms > Trier & Pilø (2012): high number of ‘false positives’ > Vletter (2015): > 80% of detected linear structures are paths

solution? > create thesauri > based on well-defined ontology of features (to be) recognized in LiDAR


FROM DETECTION TO INTERPRETATION


linear features

> straight or curved, but generally with low sinuosity > limited width, substantial length > may go against the gradient > incomplete / fragmented > intersections of linear features at sharp angles

network structures > points of departure and arrival > additional nodes in network

limited vertical expression > positive (talus) and negative (hollow ways) > sometimes very specific (ditch – talus – ditch)


AN ONTOLOGY OF PATHWAYS?

construction / practice

> intentional / non-intentional > distance of movement > environmental context > technology of movement

history of usage / function > changes in usage > multiple uses > ownership

afterlife

> (partial) destruction > erosion / sedimentation

MORPHOLOGY TRAJECTORY

Dessin, B. Poire

photograph: L. Nuninger

LIEPPEC ® MSHE C.N. Ledoux - LEA ModeLTER, L. Nuninger


Bofinger, J. & R. Hesse, 2011. “As far as the laser can reach… Laminar analysis of LiDAR detected structures as a powerful instrument for archaeological heritage management in Baden-Württemberg, Germany”. In: Cowley, D.C. (ed.), Remote Sensing for Archaeological Heritage Management. Brussels, EAC, pp. 161-171.

Doneus, M. 2013. “Openness as Visualization Technique for Interpretative Mapping of Airborne Lidar Derived Digital Terrain Models.” Remote Sensing 5 (12): 6427–6442.

Kokalj, Ž., K. Zakšek & K. Oštir 2010. “Archaeological Application of an Advanced Visualisation Technique Based on Diffuse Illumination”. In: Reuter, R. (ed.), Proceedings of the 30th EARSeL Symposium: Remote Sensing for Science, Education and Culture. Paris, EARSeL, pp. 113-120.

Pingel, T.J., K. Clarke & A. Ford, 2015. “Bonemapping: a LiDAR processing and visualization technique in support of archaeology under the canopy”. Cartography and Geographic Information Science 42 (S1): S18–S26.

Trier, Ø.D, & L.H. Pilø, 2012. “Automatic Detection of Pit Structures in Airborne Laser Scanning Data”. Archaeological Prospection 19: 103-121.

Trier, Ø., M. Zortea & C. Tonning, 2015. “Automatic detection of mound structures in airborne laser scanning data”. Journal of Archaeological Science: Reports 2: 69-79.

Vletter, W. F. 2015. “A workflow for (Semi) automatic extraction of roads and paths in forested areas from airborne laser scan data”. AARGNews 50: 33-40.

Yokoyama, R., M. Sirasawa &. R.J. Pike, 2002. “Visualizing topography by openness: A new application of image processing to digital elevation models”. Photogramm. Eng. Remote Sens. 68: 257–265.

Zingman, I., D. Saupe & K. Lambers, 2015. “Detection of incomplete enclosures of rectangular shape in remotely sensed images”. Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 87-96.


REFERENCES

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TECHNIQUES TO ENHANCE THE DETECTION OF PATHS WITH LIDAR · PDF filetechniques to enhance the detection of paths with lidar . seminar ‘detection and modelling of ancient pathways’