EDBT 2026 Demo / reviewers in the wild / expert
Adam P. Wijker
dblp:394/7908
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Segmentation and scene understanding · 50% 3D vision · 50% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Segmentation and scene understanding
semantic segmentation |
0.8 | 1 | 2024 | Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era · NeurIPS 2024 |
Computational social science and digital humanities › cultural heritage
digital archaeology |
0.2 | 1 | 2024 | Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era · NeurIPS 2024 |
Methods — techniques the papers use, named apart from their topics
deep learning · 1.5benchmark · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning EraabstractAirborne Laser Scanning (ALS) technology has transformed modern archaeology by unveiling hidden landscapes beneath dense vegetation. However, the lack of expert-annotated, open-access resources has hindered the analysis of ALS data using advanced deep learning techniques. We address this limitation with Archaeoscape (available at https://archaeoscape.ai/data/2024), a novel large-scale archaeological ALS dataset spanning 888 km² in Cambodia with 31,141 annotated archaeological features from the Angkorian period. Archaeoscape is over four times larger than comparable datasets, and the first ALS archaeology resource with open-access data, annotations, and models.We benchmark several recent segmentation models to demonstrate the benefits of modern vision techniques for this problem and highlight the unique challenges of discovering subtle human-made structures under dense jungle canopies. By making Archaeoscape available in open access, we hope to bridge the gap between traditional archaeology and modern computer vision methods. Yohann Perron, Vladyslav Sydorov, Adam P. Wijker, Damian Evans, Christophe Pottier, Loïc Landrieu |
NeurIPS | 3 |