activity
20192022
most citedFocusing on Shadows for Predicting Heightmaps from Single Remotely Sensed RGB Images with Deep Learning

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV2022

A model-agnostic approach for generating Saliency Maps to explain inferred decisions of Deep Learning Models

Savvas Karatsiolis, Andreas Kamilaris

The widespread use of black-box AI models has raised the need for algorithms and methods that explain the decisions made by these models. In recent years, the AI research community…

cs.CV20211 cited

Focusing on Shadows for Predicting Heightmaps from Single Remotely Sensed RGB Images with Deep Learning

Savvas Karatsiolis, Andreas Kamilaris

Estimating the heightmaps of buildings and vegetation in single remotely sensed images is a challenging problem. Effective solutions to this problem can comprise the stepping stone…

cs.CY2021

EscapeWildFire: Assisting People to Escape Wildfires in Real-Time

Andreas Kamilaris, Jean-Baptiste Filippi, Chirag Padubidri +5

Over the past couple of decades, the number of wildfires and area of land burned around the world has been steadily increasing, partly due to climatic changes and global warming. T…

cs.CV2020

The pursuit of beauty: Converting image labels to meaningful vectors

Savvas Karatsiolis, Andreas Kamilaris

A challenge of the computer vision community is to understand the semantics of an image, in order to allow image reconstruction based on existing high-level features or to better a…

cs.CV2020

Identification of Tree Species in Japanese Forests based on Aerial Photography and Deep Learning

Sarah Kentsch, Savvas Karatsiolis, Andreas Kamilaris +2

Natural forests are complex ecosystems whose tree species distribution and their ecosystem functions are still not well understood. Sustainable management of these forests is of hi…

cs.CV2019

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles

Andreas Kamilaris, Corjan van den Brink, Savvas Karatsiolis

This paper describes preliminary work in the recent promising approach of generating synthetic training data for facilitating the learning procedure of deep learning (DL) models, w…