Publications by Devis Tuia | EPFL Graph Search

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As unconventional sources of geoinformation, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multiperspective stream, but with unknown and diverse quality. Due to its complementarity to rem ...

A semisupervised kernel deformation function, including spatial similarity, is proposed for the classification of remote sensing (RS) images. The method exploits the characteristic of these images, in which spatially nearby points are likely to belong to t ...

When dealing with supervised target detection, the acquisition of labeled samples is one of the most critical phases: the samples must be yet representative of the class of interest, but must also be found among a vast majority of non-target examples. More ...

In this paper, we propose a methodology for the estimation of the pose of oblique landscape images. Knowledge about the pose is needed for using such images in augmented reality applications or to allow projection of pixels in a GIS for spatial analysis. W ...

A data-driven approach to the classification of hydrometeors from measurements collected with polarimetric weather radars is proposed. In a first step, the optimal number of hydrometeor classes (nopt) that can be reliably identified from a large set of pol ...

Species distribution models (SDMs) relate species occurrence data with environmental variables and are used to understand and predict species distributions across landscapes. While some machine learning models have been adopted by the SDM community, recent ...

Coffee production serves as an important source of income for millions of farmers across many tropical countries. Given the scale of the production, accurate and up-to-date maps of coffee plantations are needed to detect, monitor, and mitigate potential ne ...

Remote sensing satellites capture the cyclic dynamics of our Planet in regular time intervals recorded in satellite time series data. End-to-end trained deep learning models use this time series data to make predictions at a large scale, for instance, to p ...

When dealing with change detection problems, information about the nature of the changes is often unavailable. In this paper we propose a solution to perform unsupervised change detection based on nonlinear support vector clustering. We build a series of n ...

In this paper, we propose to estimate soil moisture in bare soils directly from hyperspectral imagery using support vector regression (nu-SVR). nu-SVR is a supervised non-parametric learning technique, e.g. making no assumption on the underlying data distr ...

MammAlps is a multimodal and multi-view dataset of wildlife behavior monitoring. Nine camera-traps were placed at three monitoring sites in the Swiss National Park, from which we curated over 14 hours of video with audio, 2D segmentation maps and 8.5 hours ...

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 Google StreetView (GSV) to Flickr images. Our results show that GSV predicts th ...

We propose a robust method for registering high oblique images of landscapes. Typically, an input image can be registered by matching it against a set of registered images of the same location. While this has been shown to work very well for images of popu ...

Fine-grained population estimation is important for several domains, such as urban planning, public health, and humanitarian action. Due to limited resources, population maps with sufficient spatial resolution and temporal frequency are not available for m ...

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 country-wise yearly survey based on a set of indicators combined into a liv ...

Remote sensing visual question answering (RSVQA) opens new avenues to promote the use of satellites data, by interfacing satellite image analysis with natural language processing. Capitalizing on the remarkable advances in natural language processing and c ...

When applied to new datasets, acquired at different time moments, with different sensors or under different acquisition conditions, deep learning models might fail spectacularly. This is because they have learned from the data distribution observed during ...

To retrieve Surface Solar Radiation (SSR) from satellite images, a baseline reflectance of the observed ground in clear-sky conditions, also called background reflectance, is necessary to distinguish atmospheric absorption and scattering effects from surfa ...

The goal of habitat suitability mapping is to predict the lo-cations in which a given species could be present. This is typically accomplished by statistical models which use envi-ronmental variables to predict species observation data. The relationship be ...

Monitoring the cracks in walls, roads and other types of infrastructure is essential to ensure the safety of a structure, and plays an important role in structural health monitoring. Automatic visual inspection allows an efficient, costeffective and safe h ...

Devis Tuia

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Geoinformation Harvesting From Social Media Data: A community remote sensing approach

Semisupervised Classification of Remote Sensing Images With Hierarchical Spatial Similarity

Discovering single classes in remote sensing images with active learning

Pose estimation of landscape images using DEM and orthophotos

Hydrometeor classification from polarimetric radar measurements: a clustering approach

Exploring neural networks and their potential for species distribution modeling

Semantic Segmentation of Coffee Plantations from Sentinel-2 Time Series

End-to-end learned early classification of time series for in-season crop type mapping

Unsupervised Change Detection via Hierarchical Support Vector Clustering

Estimation of Soil Moisture from Airborne Hyperspectral Imagery with Support Vector Regression

MammAlps: A multi-view video behavior monitoring dataset of wild mammals in the Swiss Alps

Cross-Modal Learning of Housing Quality in Amsterdam

Pose Estimation of Web-Shared Landscape Pictures

Estimating Fine-Grained Population Growth Rates from Coarse Census Data

Time Series Analysis of Urban Liveability

Language Transformers for Remote Sensing Visual Question Answering

Deep Domain Adaptation in Earth Observation

Retrieval of Surface Solar Radiation through Implicit Albedo Recovery from Temporal Context

Training Techniques for Presence-Only Habitat Suitability Mapping with Deep Learning

Segmenting Without Annotating: Crack Segmentation and Monitoring via Post-Hoc Classifier Explanations