Sara Björk defending her PhD work
Image:
Harald Lykke

Sara Björk defending her PhD work

Successful PhD Defence by Sara Björk

Sara Marie Björk defended her PhD thesis “Deep convolutional regression modelling for forest parameter retrieval” on October 6th 2023 at UiT The Arctic University of Norway.

Solid PhD defense at the Visual Intelligence Research Centre

We proudly congratulate Sara Maria Björk with her excellent defense of her PhD thesis “Deep convolutional regression modelling for forest parameter retrieval” on October 6th 2023 at UiT The Arctic University of Norway. Sara Börk was affiliated with the Visual Intelligence research center during her PhD work, and is now a KSAT employee.

Summary of thesis:

Accurate forest monitoring is crucial as forests are major global carbon sinks. Additionally, accurate prediction of forest parameters, such as forest biomass and stem volume (SV), has economic importance. Therefore, the development of regression models for forest parameter retrieval is essential.

Existing forest parameter estimation methods use regression models that establish pixel-wise relationships between ground reference data and corresponding pixels in remote sensing (RS) images. However, these models often overlook spatial contextual relationships among neighbouring pixels, limiting the potential for improved forest monitoring. The emergence of deep convolutional neural networks (CNNs) provides opportunities for enhanced forest parameter retrieval through their convolutional filters that allow for contextual modelling. However, utilising deep CNNs for regression presents its challenges. One significant challenge is that the training of CNNs typically requires continuous data layers for both predictor and response variables. While RS data is continuous, the ground reference data is sparse and scattered across large areas due to the challenges and costs associated with in situ data collection.

This thesis tackles challenges related to using CNNs for regression by introducing novel deep learning-based solutions across diverse forest types and parameters. To address the sparsity of available reference data, RS-derived prediction maps can be used as auxiliary data to train the CNN-based regression models. This is addressed through two different approaches.

Regression U-Net architecture used for image-to-image translation between Sentinel-1 (SAR) input and Lidar-derived and ground reference data of stem volume as pseudo-targets and targets

Above: Illustration on how conventional statistical or ML-based regression models (f in the image) perform regression between single pixels of Lidar measurements and a sparse set of ground reference measurements of AGB (biomass) or forest stem volume (SV). As these models considers each pixel in the input data individually, these models are non-contextual. A Lidar-derived AGB or SV prediction map can be created by use of the regression model f.
Below: Illustration of how a CNN-based regression model (g) utilise the neigbourhood of pixels through the convolutional filters in the learning of the realionship between input data of Sentine-1 (SAR scenes) and a lidar-derived prediction map as a target. Due to the convolutional filters, the CNN-based regression models are contextual

Dean Arne Smalås, Professor Anthony Paul Doulgeries, Dr. Sara Björk, Dr. Oleg Antropov, Professor Stian Normann Anfinsen and Dr. Alba Ordonez (Back)

Evaluation Committee:

  • Dr. Oleg Antropov, senior researcher at VTT Technical Research Center of Finland (1. Opponent)
  • Dr. Alba Ordonez, senior researcher at Norwegian Computing Center (2. Opponent)
  • Professor Anthony Paul Doulgeries, dept. of Physics and Technology, UiT (internal member and committee leader)

Supervisors:

  • Professor Stian Normann Anfinsen, dept. of Physics and Technology, UiT (main supervisor)
  • Professor Robert Jenssen, dept. of Physics and Technology, UiT

The Master of ceremony was Professor Arne Smalås, Dean of the Faculty of Science and Technology, UiT.

Download the thesis HERE

Latest news

New principal investigators at Visual Intelligence

February 15, 2024

Visual Intelligence congratulates associate professors Elisabeth Wetzer, Ali Ramezani-Kebrya, and Kristoffer Wickstrøm with their recently promoted roles as principal investigators at the research centre.

Two Visual Intelligence papers accepted for prestigious AI conference

February 9, 2024

New information theories and divergences by Visual Intelligence have been developed and accepted in the prestigious International Conference on Learning Representations (ICLR) 2024. ICLR has an acceptance rate of approximately 30 percent.

Taught graph machine learning at the Geilo Winter School

February 8, 2024

Associate professor Benjamin Ricaud was one of three invited lecturers at the 24th Geilo Winter School. His presentations focused on graph spectral theory, graph signal processing and their connection to graph machine learning.

TV2: Facebook to tag AI-generated pictures: - Major news

February 8, 2024

In a Norwegian news story by TV2, associate professor Kristoffer Wickstrøm shares his views on Meta's recent statements saying they will tag all AI-generated pictures published on their platforms.

TV2: Experts warn: - Makes you a felon

February 5, 2024

In a news story by TV2, associate professor Elisabeth Wetzer at UiT Machine Learning Group addresses the accessibility of deepfake tools and the necessity of proper AI legislation to combat the production and spread of such materials.

Visual Intelligence represented at Arctic Frontiers 2024

January 30, 2024

Visual Intelligence (VI) director Robert Jenssen represented the research centre during the Arctic Frontiers panel discussion, "Tilpasning til fremtidens næringsliv: kunnskap og kunstig intelligens".

Meet Petter, our new science communicator

January 26, 2024

His core work tasks involve communicating research activity from Visual Intelligence and UiT Machine Learning Group, as well as being a facilitative resource for science communication. - I am very excited to get acquainted with our researchers and their projects, says Bjørklund.

Thank you for participating in NLDL 2024!

January 16, 2024

Thank you to our participants who traveled to Tromsø to attend this year’s Northern Lights Deep Learning Conference (NLDL) from the 9th to 11th of January!

Pratet om kunstig intelligens og NLDL-konferansen på NRK Radio

January 5, 2024

I morgen tidlig kunne du høre senterleder for Visual Intelligence, Robert Jenssen prate om kunstig intelligens og NLDL-konferansen på NRKs radiosending. Hør hele NRK-intervjuet med Jenssen her (tidsstempel 01:30:00):

Kunstig intelligens: Advarer mot diskriminering av minoriteter

December 19, 2023

Helsevesenet er om bord når KI-toget nå forlater stasjonen. Men kan vi stole på at kunstig intelligens driver likebehandling av pasienter? – Det finnes en risiko for diskriminering, og minoritetsgrupper er særlig utsatt, advarer Mathias Karlsen Hauglid.

Change of Chair of the Visual intelligence Board

December 11, 2023

Gudmundur Jøkulsson, Kongsberg Satellite Services succeeds Anita Schumacher, CEO University Hospital of North Norway as chair of the Visual intelligence Board from 2024.

Visit from The Computer Vision Lab, University of Victoria, Canada

November 25, 2023

The Visual Intelligence group at NR recently had a visit by from Mélissa Côté, Research Associate at the Computer Vision Lab to discuss deep learning and marine acoustic data.

Ja takk til «krysskulturelle» prosjekter drevet fram av teknologiutvikling

November 21, 2023

Forskningsrådet står ovenfor en krevende oppgave med å blidgjøre et stort og aktivt KI-miljø i Norge. Vi håper at eksemplene på vellykket samarbeid mellom humanister og teknologer kan være til inspirasjon i prosessen