1 code implementation • 8 Nov 2022 • Nando Metzger, John E. Vargas-Muñoz, Rodrigo C. Daudt, Benjamin Kellenberger, Thao Ton-That Whelan, Ferda Ofli, Muhammad Imran, Konrad Schindler, Devis Tuia
Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations.
no code implementations • 1 Nov 2022 • Xiao Xiang Zhu, Yuanyuan Wang, Mrinalini Kochupillai, Martin Werner, Matthias Häberle, Eike Jens Hoffmann, Hannes Taubenböck, Devis Tuia, Alex Levering, Nathan Jacobs, Anna Kruspe, Karam Abdulahhad
In this article, we address key aspects in the field, including data availability, analysis-ready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data.
no code implementations • 25 Oct 2021 • Devis Tuia, Benjamin Kellenberger, Sara Beery, Blair R. Costelloe, Silvia Zuffi, Benjamin Risse, Alexander Mathis, Mackenzie W. Mathis, Frank van Langevelde, Tilo Burghardt, Roland Kays, Holger Klinck, Martin Wikelski, Iain D. Couzin, Grant van Horn, Margaret C. Crofoot, Charles V. Stewart, Tanya Berger-Wolf
Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices.
no code implementations • 24 Sep 2021 • Christel Chappuis, Sylvain Lobry, Benjamin Kellenberger, Bertrand Le Saux, Devis Tuia
Visual question answering (VQA) has recently been introduced to remote sensing to make information extraction from overhead imagery more accessible to everyone.
1 code implementation • 17 Aug 2021 • Xiaochen Zheng, Benjamin Kellenberger, Rui Gong, Irena Hajnsek, Devis Tuia
In detail, we examine a combination of recent contrastive learning methodologies like Momentum Contrast (MoCo) and Cross-Level Instance-Group Discrimination (CLD) to condition our model on the aerial images without the requirement for labels.
no code implementations • 29 Jul 2021 • Benjamin Kellenberger, John E. Vargas-Muñoz, Devis Tuia, Rodrigo C. Daudt, Konrad Schindler, Thao T-T Whelan, Brenda Ayo, Ferda Ofli, Muhammad Imran
Building functions shall be retrieved by parsing social media data like for instance tweets, as well as ground-based imagery, to automatically identify different buildings functions and retrieve further information such as the number of building stories.
no code implementations • 15 Apr 2021 • Devis Tuia, Michele Volpi, Maxime Trolliet, Gustau Camps-Valls
We introduce a method for manifold alignment of different modalities (or domains) of remote sensing images.
no code implementations • 15 Apr 2021 • Devis Tuia, Claudio Persello, Lorenzo Bruzzone
The success of supervised classification of remotely sensed images acquired over large geographical areas or at short time intervals strongly depends on the representativity of the samples used to train the classification algorithm and to define the model.
no code implementations • 15 Apr 2021 • Devis Tuia, Michele Volpi, Loris Copa, Mikhail Kanevski, Jordi Munoz-Mari
Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines.
no code implementations • 15 Apr 2021 • Devis Tuia, Jordi Munoz-Mari
In this paper, we study the applicability of active learning in operative scenarios: more particularly, we consider the well-known contradiction between the active learning heuristics, which rank the pixels according to their uncertainty, and the user's confidence in labeling, which is related to both the homogeneity of the pixel context and user's knowledge of the scene.
1 code implementation • 11 Apr 2021 • Devis Tuia, Ribana Roscher, Jan Dirk Wegner, Nathan Jacobs, Xiao Xiang Zhu, Gustau Camps-Valls
In the last years we have witnessed the fields of geosciences and remote sensing and artificial intelligence to become closer.
no code implementations • 20 Jan 2021 • Marrit Leenstra, Diego Marcos, Francesca Bovolo, Devis Tuia
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.
Ranked #4 on Change Detection on OSCD - 13ch (using extra training data)
1 code implementation • 10 Jan 2021 • Yuansheng Hua, Diego Marcos, Lichao Mou, Xiao Xiang Zhu, Devis Tuia
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-consuming to produce.
no code implementations • 9 Dec 2020 • Devis Tuia, Benjamin Kellenberger, Adrian Pérez-Suay, Gustau Camps-Valls
With a single model, we are able to outline clouds along all year and during day and night with high accuracy.
no code implementations • 7 Dec 2020 • Devis Tuia, Diego Marcos, Gustau Camps-Valls
Remote sensing image classification exploiting multiple sensors is a very challenging problem: data from different modalities are affected by spectral distortions and mis-alignments of all kinds, and this hampers re-using models built for one image to be used successfully in other scenes.
1 code implementation • 18 Sep 2020 • Diego Marcos, Ruth Fong, Sylvain Lobry, Remi Flamary, Nicolas Courty, Devis Tuia
Once the attributes are learned, they can be re-combined to reach the final decision and provide both an accurate prediction and an explicit reasoning behind the CNN decision.
no code implementations • 17 Sep 2020 • John E. Vargas-Muñoz, Devis Tuia, Alexandre X. Falcão
Locating populations in rural areas of developing countries has attracted the attention of humanitarian mapping projects since it is important to plan actions that affect vulnerable areas.
no code implementations • 13 Jul 2020 • John Vargas, Shivangi Srivastava, Devis Tuia, Alexandre Falcao
OpenStreetMap (OSM) is a community-based, freely available, editable map service that was created as an alternative to authoritative ones.
no code implementations • 24 Apr 2020 • Alex Levering, Martin Tomko, Devis Tuia, Kourosh Khoshelham
In this paper we propose a system based on an off-the-shelf deep neural network architecture that is able to detect and recognize types of unsigned (non-placarded, such as traffic signs), physical (visible in images) road incidents.
no code implementations • 16 Mar 2020 • Sylvain Lobry, Diego Marcos, Jesse Murray, Devis Tuia
We report the results obtained by applying a model based on Convolutional Neural Networks (CNNs) for the visual part and on a Recurrent Neural Network (RNN) for the natural language part to this task.
no code implementations • 18 Sep 2019 • Diego Marcos, Sylvain Lobry, Devis Tuia
This gives the user insight into what the model has seen, where, and a final output directly linked to this information in a comprehensive and interpretable way.
no code implementations • 23 Jul 2019 • Shivangi Srivastava, Maxim Berman, Matthew B. Blaschko, Devis Tuia
The latter approach falls under the denomination of lifelong learning, where the model is updated in a way that it performs well on both old and new tasks, without having access to the old task's training samples anymore.
no code implementations • 17 Jul 2019 • Benjamin Kellenberger, Diego Marcos, Sylvain Lobry, Devis Tuia
We present an Active Learning (AL) strategy for re-using a deep Convolutional Neural Network (CNN)-based object detector on a new dataset.
no code implementations • 5 May 2019 • Shivangi Srivastava, John E. Vargas-Muñoz, Devis Tuia
Landuse characterization is important for urban planning.
1 code implementation • 8 Apr 2019 • Kilian Fatras, Bharath Bhushan Damodaran, Sylvain Lobry, Rémi Flamary, Devis Tuia, Nicolas Courty
Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping.
2 code implementations • 30 Jan 2019 • Marc Rußwurm, Nicolas Courty, Rémi Emonet, Sébastien Lefèvre, Devis Tuia, Romain Tavenard
In this work, we present an End-to-End Learned Early Classification of Time Series (ELECTS) model that estimates a classification score and a probability of whether sufficient data has been observed to come to an early and still accurate decision.
no code implementations • 24 Jan 2019 • John E. Vargas-Muñoz, Sylvain Lobry, Alexandre X. Falcão, Devis Tuia
Rural building mapping is paramount to support demographic studies and plan actions in response to crisis that affect those areas.
no code implementations • 24 Aug 2018 • Devis Tuia, Michele Volpi, Gabriele Moser
In this paper, we follow these two observations and encode them as priors in an energy minimization framework based on conditional random fields (CRFs), where classification results obtained at pixel and region levels are probabilistically fused.
no code implementations • 23 Aug 2018 • Michele Volpi, Devis Tuia
When approaching the semantic segmentation of overhead imagery in the decimeter spatial resolution range, successful strategies usually combine powerful methods to learn the visual appearance of the semantic classes (e. g. convolutional neural networks) with strategies for spatial regularization (e. g. graphical models such as conditional random fields).
no code implementations • 31 Jul 2018 • Diego Marcos, Benjamin Kellenberger, Sylvain Lobry, Devis Tuia
We study the effect of injecting local scale equivariance into Convolutional Neural Networks.
1 code implementation • 25 Jul 2018 • Zhenchao Zhang, George Vosselman, Markus Gerke, Devis Tuia, Michael Ying Yang
Detecting topographic changes in the urban environment has always been an important task for urban planning and monitoring.
no code implementations • 29 Jun 2018 • Benjamin Kellenberger, Diego Marcos, Devis Tuia
In this paper, we study how to scale CNNs to large wildlife census tasks and present a number of recommendations to train a CNN on a large UAV dataset.
1 code implementation • 17 May 2018 • Ilke Demir, Krzysztof Koperski, David Lindenbaum, Guan Pang, Jing Huang, Saikat Basu, Forest Hughes, Devis Tuia, Ramesh Raskar
We present the DeepGlobe 2018 Satellite Image Understanding Challenge, which includes three public competitions for segmentation, detection, and classification tasks on satellite images.
4 code implementations • ECCV 2018 • Bharath Bhushan Damodaran, Benjamin Kellenberger, Rémi Flamary, Devis Tuia, Nicolas Courty
In computer vision, one is often confronted with problems of domain shifts, which occur when one applies a classifier trained on a source dataset to target data sharing similar characteristics (e. g. same classes), but also different latent data structures (e. g. different acquisition conditions).
no code implementations • 16 Mar 2018 • Diego Marcos, Michele Volpi, Benjamin Kellenberger, Devis Tuia
In remote sensing images, the absolute orientation of objects is arbitrary.
2 code implementations • CVPR 2018 • Diego Marcos, Devis Tuia, Benjamin Kellenberger, Lisa Zhang, Min Bai, Renjie Liao, Raquel Urtasun
The world is covered with millions of buildings, and precisely knowing each instance's position and extents is vital to a multitude of applications.
3 code implementations • 13 Mar 2018 • Ievgen Redko, Nicolas Courty, Rémi Flamary, Devis Tuia
In this paper, we propose to tackle the problem of reducing discrepancies between multiple domains referred to as multi-source domain adaptation and consider it under the target shift assumption: in all domains we aim to solve a classification problem with the same output classes, but with labels' proportions differing across them.
1 code implementation • 11 Oct 2017 • Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia, Liangpei Zhang, Feng Xu, Friedrich Fraundorfer
In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with.
no code implementations • 6 Sep 2017 • Nicolas Rey, Michele Volpi, Stéphane Joost, Devis Tuia
Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife monitoring, with several advantages over traditional field-based methods.
no code implementations • 23 May 2017 • Sébastien Lefèvre, Devis Tuia, Jan Dirk Wegner, Timothée Produit, Ahmed Samy Nassar
In this paper, we discuss and review how combined multi-view imagery from satellite to street-level can benefit scene analysis.
3 code implementations • ICCV 2017 • Diego Marcos, Michele Volpi, Nikos Komodakis, Devis Tuia
In many computer vision tasks, we expect a particular behavior of the output with respect to rotations of the input image.
Ranked #8 on Multi-tissue Nucleus Segmentation on Kumar
Breast Tumour Classification Colorectal Gland Segmentation: +5
no code implementations • 2 Aug 2016 • Michele Volpi, Devis Tuia
In this paper we present a CNN-based system relying on an downsample-then-upsample architecture.
no code implementations • 23 Jun 2016 • Devis Tuia, Rémi Flamary, Nicolas Courty
In this paper, we tackle the question of discovering an effective set of spatial filters to solve hyperspectral classification problems.
1 code implementation • 23 Jun 2016 • Devis Tuia, Remi Flamary, Michel Barlaud
In this paper, we study the effect of different regularizers and their implications in high dimensional image classification and sparse linear unmixing.
no code implementations • CVPR 2016 • Diego Marcos, Raffay Hamid, Devis Tuia
The growing availability of very high resolution (<1 m/pixel) satellite and aerial images has opened up unprecedented opportunities to monitor and analyze the evolution of land-cover and land-use across the world.
1 code implementation • 22 Apr 2016 • Diego Marcos, Michele Volpi, Devis Tuia
We present a method for learning discriminative filters using a shallow Convolutional Neural Network (CNN).
no code implementations • 31 Jan 2016 • Valero Laparra, Sandra Jiménez, Devis Tuia, Gustau Camps-Valls, Jesús Malo
Moreover, PPA shows a number of interesting analytical properties.
no code implementations • 2 Jul 2015 • Nicolas Courty, Rémi Flamary, Devis Tuia, Alain Rakotomamonjy
Domain adaptation from one data space (or domain) to another is one of the most challenging tasks of modern data analytics.
1 code implementation • 9 Apr 2015 • Devis Tuia, Gustau Camps-Valls
We introduce a kernel method for manifold alignment (KEMA) and domain adaptation that can match an arbitrary number of data sources without needing corresponding pairs, just few labeled examples in all domains.
no code implementations • 18 Oct 2013 • Gustavo Camps-Valls, Devis Tuia, Lorenzo Bruzzone, Jón Atli Benediktsson
Hyperspectral images show similar statistical properties to natural grayscale or color photographic images.