340 citations · 1.8k across the 24 of their papers we have counts for
17 papers · 1 filter
EcoWikiRS: Learning Ecological Representation of Satellite Images from Weak Supervision with Species Observations and Wikipedia
Valerie Zermatten, Javiera Castillo-Navarro, Pallavi Jain +2
The presence of species provides key insights into the ecological properties of a location such as land cover, climatic conditions or even soil properties. We propose a method to p…
Multi-Scale Grouped Prototypes for Interpretable Semantic Segmentation
Hugo Porta, Emanuele Dalsasso, Diego Marcos +1
Prototypical part learning is emerging as a promising approach for making semantic segmentation interpretable. The model selects real patches seen during training as prototypes and…
Cross-Modal Learning of Housing Quality in Amsterdam
Alex Levering, Diego Marcos, Devis Tuia
In our research we test data and models for the recognition of housing quality in the city of Amsterdam from ground-level and aerial imagery. For ground-level images we compare Goo…
Time Series Analysis of Urban Liveability
Alex Levering, Diego Marcos, Devis Tuia
In this paper we explore deep learning models to monitor longitudinal liveability changes in Dutch cities at the neighbourhood level. Our liveability reference data is defined by a…
Masking Strategies for Background Bias Removal in Computer Vision Models
Ananthu Aniraj, Cassio F. Dantas, Dino Ienco +1
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 parti…
Semantic Segmentation of Remote Sensing Images with Sparse Annotations
Yuansheng Hua, Diego Marcos, Lichao Mou +2
Training Convolutional Neural Networks (CNNs) for very high resolution images requires a large quantity of high-quality pixel-level annotations, which is extremely labor- and time-…
Self-supervised pre-training enhances change detection in Sentinel-2 imagery
Marrit Leenstra, Diego Marcos, Francesca Bovolo +1
While annotated images for change detection using satellite imagery are scarce and costly to obtain, there is a wealth of unlabeled images being generated every day. In order to le…
Contextual Semantic Interpretability
Diego Marcos, Ruth Fong, Sylvain Lobry +3
Convolutional neural networks (CNN) are known to learn an image representation that captures concepts relevant to the task, but do so in an implicit way that hampers model interpre…
RSVQA: Visual Question Answering for Remote Sensing Data
Sylvain Lobry, Diego Marcos, Jesse Murray +1
This paper introduces the task of visual question answering for remote sensing data (RSVQA). Remote sensing images contain a wealth of information which can be useful for a wide ra…
Semantically Interpretable Activation Maps: what-where-how explanations within CNNs
Diego Marcos, Sylvain Lobry, Devis Tuia
A main issue preventing the use of Convolutional Neural Networks (CNN) in end user applications is the low level of transparency in the decision process. Previous work on CNN inter…
Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning
Benjamin Kellenberger, Diego Marcos, Sylvain Lobry +1
We present an Active Learning (AL) strategy for re-using a deep Convolutional Neural Network (CNN)-based object detector on a new dataset. This is of particular interest for wildli…
Scale equivariance in CNNs with vector fields
Diego Marcos, Benjamin Kellenberger, Sylvain Lobry +1
We study the effect of injecting local scale equivariance into Convolutional Neural Networks. This is done by applying each convolutional filter at multiple scales. The output is a…
Detecting Mammals in UAV Images: Best Practices to address a substantially Imbalanced Dataset with Deep Learning
Benjamin Kellenberger, Diego Marcos, Devis Tuia
Knowledge over the number of animals in large wildlife reserves is a vital necessity for park rangers in their efforts to protect endangered species. Manual animal censuses are dan…
Learning deep structured active contours end-to-end
Diego Marcos, Devis Tuia, Benjamin Kellenberger +4
The world is covered with millions of buildings, and precisely knowing each instance's position and extents is vital to a multitude of applications. Recently, automated building fo…
Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models
Diego Marcos, Michele Volpi, Benjamin Kellenberger +1
In remote sensing images, the absolute orientation of objects is arbitrary. Depending on an object's orientation and on a sensor's flight path, objects of the same semantic class c…
Rotation equivariant vector field networks
Diego Marcos, Michele Volpi, Nikos Komodakis +1
In many computer vision tasks, we expect a particular behavior of the output with respect to rotations of the input image. If this relationship is explicitly encoded, instead of tr…
Learning rotation invariant convolutional filters for texture classification
Diego Marcos, Michele Volpi, Devis Tuia
We present a method for learning discriminative filters using a shallow Convolutional Neural Network (CNN). We encode rotation invariance directly in the model by tying the weights…