AI for the environment

This page contains information about projects, publications and other things that relate specifically to my research on ML systems for various environmental applications (both regarding mitigation of further environmental harm, as well as adaptation with respect to existing or locked in harm). It is thus in a sense a subset of my full professional bio which can be found on the main page.

Publications and preprints

GOMAA-Geo: GOal Modality Agnostic Active Geo-localization (NeurIPS 2024, to appear)Code

Impacts of Color and Texture Distortions on Earth Observation Data in Deep Learning (2nd ML-for-RS Workshop at ICLR 2024)

NBS Initiative Position Paper: Embracing Nature-Based Solutions for Sustainable Development (2024) – Presented at the ECTP conference 2024

Creating and Leveraging a Synthetic Dataset of Cloud Optical Thickness Measures for Cloud Detection in MSI (Remote Sensing 2024)arXivCode & Data – Also presented (oral) at the 2nd ML-for-RS Workshop at ICLR 2024 – Fun fact: ML models developed in this project have been orbiting in space, via a collaboration with Unibap and D-orbit

Fully Convolutional Networks for Dense Water Flow Intensity Prediction in Swedish Catchment Areas (SAIS 2023)arXivCodeVideoPopular summary

Aerial View Localization with Reinforcement Learning: Towards Emulating Search-and-Rescue (1st ML-for-RS Workshop at ICLR 2023)SAIS 2023 paperML4RS workshop paperCodeVideo

Few-shot Bioacoustic Event Detection using a Prototypical Network Ensemble with Adaptive Embedding Functions (DCASE Workshop 2022)CodePopular summary

Media and public communication

November 2024: Gave a presentation and participated in a panel discussion on the theme Is technology our only salvation from climate collapse? at Internetdagarna 2024.

October 2024: Guest lecture for a class of master students at Lund University on the theme AI for the environment.

August 2024: Participated in a panel discussion titled Science communication and AI – threats, challenges and opportunities, at the World Water Week in Stockholm.

August 2024: Invited presentation for Sally Dobberman on the topic AI considerations under post-growth scenarios.

January 2024: Commentary on AI for tackling climate change (Naturvetarna, in Swedish).

December 2023: Guest lecture for a class of master students at Lund University on the theme AI for the environment. Similar presentation also for Digitaliseringskonsulterna.

October 2023: Presentation for Lantbrukarnas Riksförbund (Federation of Swedish Farmers) about AI within the agricultural sector. Title: AI within horticulture: Where we are today, and where we may be headed.

September 2023: Commentary on AI for tackling climate change (Miljö och utveckling, in Swedish)

September 2022: AI for climate (Naturvetarna podcast, in Swedish)Extended conversation

May 2022: Keynote presentation about AI for tackling the climate challenges at Stockholm Tech.

January 2022: AI and climate change (RISE Learning Machines)Article about the talk

Past and ongoing projects

Dec 2024 - Dec 2026 (starting soon): Grey to green: Using AI to detect and prioritize conversion of impervious surfaces to multifunctional nature-based solutions (advisor). Funded by FORMAS.

May 2024 - May 2026 (ongoing): AI-based Power Production Models for Increased Wind Farm Efficiency together with PhD candidate Maria Bånkestad (among others). Funded by the Swedish Energy Agency.

2023: ML for cloud optical thickness estimation together with the Swedish Meteorological and Hydrological Institute (SMHI), AI Sweden, Luleå University of Technology and the Swedish Forestry Agency. The work was published in the journal Remote Sensing (2024), got accepted as an oral at the 2nd ML-for-RS Workshop at ICLR 2024, and was presented as a poster at EUMETSAT 2023. Funded by Vinnova. One of the project deliverables was a developer event (Hackathon) for university students. Code & data are available!. Fun fact: ML models developed in this project have been orbiting in space, via a collaboration with Unibap and D-orbit.

Fall 2022: Pre-study about using ML for wetland monitoring in Sweden together with and funded by the Swedish Environmental Protection Agency. Code is available here.

Past and ongoing supervision

Spring 2024: Co-main supervisor (jointly with Martin Willbo) of the master thesis students Agnes Eriksson and Malte Åhman, Lund University. Academic supervisor: Assoc. Prof. Mikael Nilsson. Thesis: The Power of Privilege: Enhancing Land Cover Classification with Privileged Information.

Spring 2024: Co-main supervisor (jointly with Dr. Olof Mogren) of the master thesis students Emma Amnemyr and Daniel Björklund, Lund University. Academic supervisor: Prof. Kalle Åström. Thesis: Active Learning Techniques for Annotation Efficiency in Detecting Coffee Berry Disease.

Summer-Fall 2023: Co-main supervisor (jointly with Dr. Olof Mogren) of the master thesis students Axel Eiman and Nils Eickhoff, Chalmers University of Technology. Thesis: Weakly semi-supervised object detection for annotation efficiency: Leveraging a mix of strong bounding box labels and weak point labels for detecting coffee berry disease. The thesis topic is within the area of climate adaptation and was conducted with collaborators in Tanzania (Mpendakazi Agribusiness), who provided data and use-cases. A popular summary can be found here.

Spring 2023: Main supervisor of the master thesis student Ennio Rampello, KTH Royal Institute of Technology. Academic supervisors: Prof. Yifang Ban and Dr. Puzhao Zhang. Thesis: High-altitude navigation to improve the performance of AiRLoc: An RL model for drone navigation. The thesis is related to the area of UAV-based disaster response and management.

Spring 2023: Main supervisor of the master thesis student Vishal Nedungadi, KTH Royal Institute of Technology. Academic supervisors: Prof. Yifang Ban and Ritu Yadav (PhD candidate). Thesis: Active street to aerial view geo-localization. The thesis is related to the area of UAV-based disaster response and management.

Spring 2022: Main supervisor of the master thesis students Anton Samuelsson and John Backsund, Lund University. Academic supervisor: Prof. Kalle Åström. Thesis: Aerial View Goal Localization with Reinforcement Learning. The thesis was subsequently condensed and accepted to a remote sensing workshop at ICLR 2023 (see Publications and preprints above). The thesis is related to the area of UAV-based disaster response and management.

Review work

I have served as a reviewer for the ISPRS Journal of Photogrammetry and Remote Sensing (2023), Remote Sensing (2023), IEEE Transactions on Geoscience and Remote Sensing (2x 2024), and the International Degrowth Conference (2024).

Grant proposals. In addition to reviewing papers, I’ve also reviewed grant proposals for Climate Change AI (2024).