Interpretable segmentation and naturalness classification of forests using the Canopy Height Model
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Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
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Sammanfattning
As increasing amounts of ecological data from remote sensing is available, so does
the opportunities to utilize this data also increase. One such opportunity is to
classify the naturalness of forests automatically from data instead of using costly
field-surveys, in the purpose of more efficiently managing natural ecosystems. Doing
this using AI-based methods in an interpretable way is the topic of this thesis.
Interpretability is of utmost importance in order to improve testing and verifying
of models as well as to provide transparency and understanding to affected parties,
such as forestry companies.
Using the Canopy Height Model (CHM), i.e. laser-scanned height data, as single
source of data, three segmentation algorithms are evaluated on the task of creating
segments of forest from CHM-rasters. A lack of ground-truth data in the evaluation
is overcome by using human feedback as preference-based pairwise comparisons to
evaluate and optimize. A framework to collect such data has been developed in the
shape of a web application to assure convenience and efficiency in collection of data.
Based on this data, one superior algorithm is selected and further optimized, to then
be applied in tandem with a previously developed naturalness classifier in order to
map the naturalness of forests.
The result is a CHM–into–naturalness pipeline with a 75% naturalness classification
accuracy, evaluated on a 50 × 50 km area in Southern Sweden, only performing
segmentation and classification on 640 × 640 m tiles of the CHM at a time. This
method is shown to have both big potential but also some vulnerabilities, among
them being the tile sizes, and remedies are suggested and discussed. A certain
mismatch in how the model was trained and how it is now evaluated introduces
some uncertainty to the evaluation, and possible improvements are discussed. In
summary, the thesis lays the ground-work for the topic and suggests important
aspects to iterate upon, giving a direction for future research.
Beskrivning
Ämne/nyckelord
interpretable, AI, forest, segmentation, preference-based, pairwise comparison, naturalness, classification