291 citations · 331 across the 10 of their papers we have counts for
8 papers · 1 filter
Geographical Context Matters: Bridging Fine and Coarse Spatial Information to Enhance Continental Land Cover Mapping
Babak Ghassemi, Cassio Fraga-Dantas, Raffaele Gaetano +4
Land use and land cover mapping from Earth Observation (EO) data is a critical tool for sustainable land and resource management. While advanced machine learning and deep learning…
Attentive Weakly Supervised land cover mapping for object-based satellite image time series data with spatial interpretation
Dino Ienco, Yawogan Jean Eudes Gbodjo, Roberto Interdonato +1
Nowadays, modern Earth Observation systems continuously collect massive amounts of satellite information. The unprecedented possibility to acquire high resolution Satellite Image T…
Combining Sentinel-1 and Sentinel-2 Time Series via RNN for object-based land cover classification
Dino Ienco, Raffaele Gaetano, Roberto Interdonato +2
Radar and Optical Satellite Image Time Series (SITS) are sources of information that are commonly employed to monitor earth surfaces for tasks related to ecology, agriculture, mobi…
DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn
Roberto Interdonato, Dino Ienco, Raffaele Gaetano +1
Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is represented by the Sentinel-2 mission, which provides images at high spa…
MRFusion: A Deep Learning architecture to fuse PAN and MS imagery for land cover mapping
Raffaele Gaetano, Dino Ienco, Kenji Ose +1
Nowadays, Earth Observation systems provide a multitude of heterogeneous remote sensing data. How to manage such richness leveraging its complementarity is a crucial chal- lenge in…
M3Fusion: A Deep Learning Architecture for Multi-{Scale/Modal/Temporal} satellite data fusion
P. Benedetti, D. Ienco, R. Gaetano +3
Modern Earth Observation systems provide sensing data at different temporal and spatial resolutions. Among optical sensors, today the Sentinel-2 program supplies high-resolution te…
Deep Recurrent Neural Networks for mapping winter vegetation quality coverage via multi-temporal SAR Sentinel-1
Dinh Ho Tong Minh, Dino Ienco, Raffaele Gaetano +4
Mapping winter vegetation quality coverage is a challenge problem of remote sensing. This is due to the cloud coverage in winter period, leading to use radar rather than optical im…
Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks
Dino Ienco, Raffaele Gaetano, Claire Dupaquier +1
Nowadays, modern earth observation programs produce huge volumes of satellite images time series (SITS) that can be useful to monitor geographical areas through time. How to effici…