activity
20042024
most citedLearning and Recognizing Archeological Features from LiDAR Data

30 citations · 31 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Climatic & Anthropogenic Hazards to the Nasca World Heritage: Application of Remote Sensing, AI, and Flood Modelling

Masato Sakai, Marcus Freitag, Akihisa Sakurai +2

Preservation of the Nasca geoglyphs at the UNESCO World Heritage Site in Peru is urgent as natural and human impact accelerates. More frequent weather extremes such as flashfloods…

cs.LG2023

TensorBank: Tensor Lakehouse for Foundation Model Training

Romeo Kienzler, Leonardo Pondian Tizzei, Benedikt Blumenstiel +9

Storing and streaming high dimensional data for foundation model training became a critical requirement with the rise of foundation models beyond natural language. In this paper we…

cs.CV2020★ 30 cited

Learning and Recognizing Archeological Features from LiDAR Data

Conrad M Albrecht, Chris Fisher, Marcus Freitag +4

We present a remote sensing pipeline that processes LiDAR (Light Detection And Ranging) data through machine & deep learning for the application of archeological feature detection…

cs.DC2020

Event Clustering & Event Series Characterization on Expected Frequency

Conrad M Albrecht, Marcus Freitag, Theodore G van Kessel +2

We present an efficient clustering algorithm applicable to one-dimensional data such as e.g. a series of timestamps. Given an expected frequency , we introduce an $\mathca…

astro-ph2004★ 1 cited

Collisions Between Single Stars in Dense Clusters: Runaway Formation of a Massive Object

Marc Freitag, M. Atakan Gürkan, Frederic A. Rasio

Using Monte Carlo codes, we follow the collisional evolution of clusters in a variety of scenarios. We consider the conditions under which a cluster of main sequence stars may unde…