Predicting Vegetation Stratum Occupancy from Airborne LiDAR Data with Deep Learning

Autor: Ekaterina Kalinicheva, Loic Landrieu, Clément Mallet, Nesrine Chehata

Publikováno v: International Journal of Applied Earth Observations and Geoinformation, Vol 112, Iss , Pp 102863- (2022)

We propose a new deep learning-based method for estimating the occupancy of vegetation strata from airborne 3D LiDAR point clouds. Our model predicts rasterized occupancy maps for three vegetation strata corresponding to lower, medium, and higher cov

Externí odkaz: https://doaj.org/article/1c256d016c3a46ae9eb4a0e4e8ef952f

Unsupervised Satellite Image Time Series Clustering Using Object-Based Approaches and 3D Convolutional Autoencoder

Autor: Ekaterina Kalinicheva, Jérémie Sublime, Maria Trocan

Publikováno v: Remote Sensing, Vol 12, Iss 11, p 1816 (2020)

Nowadays, satellite image time series (SITS) analysis has become an indispensable part of many research projects as the quantity of freely available remote sensed data increases every day. However, with the growing image resolution, pixel-level SITS

Externí odkaz: https://doaj.org/article/924ac67a95f24211919af28f7323daf5

Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami

Autor: Jérémie Sublime, Ekaterina Kalinicheva

Publikováno v: Remote Sensing, Vol 11, Iss 9, p 1123 (2019)

Post-disaster damage mapping is an essential task following tragic events such as hurricanes, earthquakes, and tsunamis. It is also a time-consuming and risky task that still often requires the sending of experts on the ground to meticulously map and

Externí odkaz: https://doaj.org/article/bf1b49a1b2ea468a87e6c174b20965db

Unsupervised Satellite Image Time Series Clustering Using Object-Based Approaches and 3D Convolutional Autoencoder

Autor: Maria Trocan, Ekaterina Kalinicheva, Jérémie Sublime

Publikováno v: Remote Sensing; Volume 12; Issue 11; Pages: 1816
Remote Sensing
Remote Sensing, MDPI, 2020, Advanced Machine Learning for Time Series Remote Sensing Data Analysis), 12 (11), ⟨10.3390/rs12111816⟩
Remote Sensing, MDPI, In press, ⟨10.3390/rs12111816⟩
Remote Sensing, Vol 12, Iss 1816, p 1816 (2020)

International audience; Nowadays, satellite image time series (SITS) analysis has become an indispensable part of many research projects as the quantity of freely available remote sensed data increases every day. However, with the growing image resol

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e30a4905ed4bc9cb236c13adf13b8ea3

Object-Based Change Detection in Satellite Images Combined with Neural Network Autoencoder Feature Extraction

Autor: Jérémie Sublime, Maria Trocan, Ekaterina Kalinicheva

Publikováno v: IPTA

Change detection is a challenging task in the field of remote sensing. While most applications use supervised change detection and classification techniques, it still remains a difficult task to create a training database for generic applications. In

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::829650923294836b1fc2c6842cc958f1
https://doi.org/10.1109/ipta.2019.8936085