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Found 18 papers, 5 papers with code
Reuse out-of-year data to enhance land cover mappingvia feature disentanglement and contrastive learning
Typically, when creating a land cover (LC) map, precise ground truth data is collected through time-consuming and expensive field campaigns.
Cartographie de l'habitat de reproduction du tétras-lyre (Lyrurus tetrix) dans les Alpes françaises
Indeed, the extraction of variables from remote sensing helped to describe the area studied at appropriate spatial and temporal scales: horizontal and vertical structure (heterogeneity), functioning (vegetation indices), phenology (seasonal or inter-annual dynamics) and biodiversity.
Hierarchical Concept Discovery Models: A Concept Pyramid Scheme
Deep Learning algorithms have recently gained significant attention due to their impressive performance.
PDiscoNet: Semantically consistent part discovery for fine-grained recognition
Fine-grained classification often requires recognizing specific object parts, such as beak shape and wing patterns for birds.
Masking Strategies for Background Bias Removal in Computer Vision Models
Models for fine-grained image classification tasks, where the difference between some classes can be extremely subtle and the number of samples per class tends to be low, are particularly prone to picking up background-related biases and demand robust methods to handle potential examples with out-of-distribution (OOD) backgrounds.
Sparse Linear Concept Discovery Models
The recent mass adoption of DNNs, even in safety-critical scenarios, has shifted the focus of the research community towards the creation of inherently intrepretable models.
Towards Explainable Land Cover Mapping: a Counterfactual-based Strategy
Furthermore, plausibility/realism of the generated counterfactual explanations is enforced via the proposed adversarial learning strategy.
Attentive Weakly Supervised land cover mapping for object-based satellite image time series data with spatial interpretation
Nowadays, modern Earth Observation systems continuously collect massive amounts of satellite information.
Fine grained classification for multi-source land cover mapping
Nowadays, there is a general agreement on the need to better characterize agricultural monitoring systems in response to the global changes.
Object-based multi-temporal and multi-source land cover mapping leveraging hierarchical class relationships
European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at highspatial resolution and high revisit time, respectively, radar and optical imagesthat support a wide range of Earth surface monitoring tasks such as LandUse/Land Cover mapping.
Supervised level-wise pretraining for recurrent neural network initialization in multi-class classification
To the best of our knowledge, despite the great interest in RNN-based classification, this is the first data-aware strategy dealing with the initialization of such models.
Combining Sentinel-1 and Sentinel-2 Time Series via RNN for object-based land cover classification
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, mobility, land management planning and land cover monitoring.
DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn
In this work, we propose the first deep learning architecture for the analysis of SITS data, namely \method{} (DUal view Point deep Learning architecture for time series classificatiOn), that combines Convolutional and Recurrent neural networks to exploit their complementarity.
MRFusion: A Deep Learning architecture to fuse PAN and MS imagery for land cover mapping
Common techniques to produce land cover maps from such VHSR images typically opt for a prior pansharpening of the multi-resolution source for a full resolution processing.
Deep Recurrent Neural Networks for mapping winter vegetation quality coverage via multi-temporal SAR Sentinel-1
The objective of this paper is to provide a better understanding of the capabilities of radar Sentinel-1 and deep learning concerning about mapping winter vegetation quality coverage.
Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks
Nowadays, modern earth observation programs produce huge volumes of satellite images time series (SITS) that can be useful to monitor geographical areas through time.
Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover source-mismatch
In this paper, we follow-up the study of Qian et al., and show that, due to intrinsic joint minimization, the results obtained from a Convolutional Neural Network (CNN) or a Fully Connected Neural Network (FNN), if well parameterized, surpass the conventional use of a RM with an EC.
