86 citations · 96 across the 4 of their papers we have counts for
5 papers
High-Resolution Forest Mapping from L-Band Interferometric SAR Time Series using Deep Learning over Northern Spain
Chiara Telli, Oleg Antropov, Anne Lönnqvist +1
In this study, we examine the potential of high-resolution forest mapping using L-band interferometric time series datasets and deep learning modeling. Our SAR data are represented…
Deep Learning Model Transfer in Forest Mapping using Multi-source Satellite SAR and Optical Images
Shaojia Ge, Oleg Antropov, Tuomas Häme +2
Deep learning (DL) models are gaining popularity in forest variable prediction using Earth Observation images. However, in practical forest inventories, reference datasets are ofte…
A Novel Semisupervised Contrastive Regression Framework for Forest Inventory Mapping with Multisensor Satellite Data
Shaojia Ge, Hong Gu, Weimin Su +2
Accurate mapping of forests is critical for forest management and carbon stocks monitoring. Deep learning is becoming more popular in Earth Observation (EO), however, the availabil…
ICEYE microsatellite SAR constellation status update: Evaluation of first commercial imaging modes
Vladimir Ignatenko, Pekka Laurila, Andrea Radius +3
The ICEYE constellation features the first operational microsatellite based X-band SAR sensors suitable for all weather day-and-night Earth Observation. In this paper, we report on…
Wide-Area Land Cover Mapping with Sentinel-1 Imagery using Deep Learning Semantic Segmentation Models
Sanja Šćepanović, Oleg Antropov, Pekka Laurila +3
Land cover mapping is essential to monitoring the environment and understanding the effects of human activities on it. The automatic approaches to land cover mapping (i.e., image s…