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Image-based retrieval in large Earth observation archives is challenging because one needs to navigate across thousands of candidate matches only with the query image as a guide. By using text as information supporting the visual query, the retrieval syste ...

Semantic segmentation consists of the generation of a categorical map, given an image in which each pixel of the image is automatically assigned a class. Deep learning allows the influence of the pixel's context to be learned by capturing the non-linear re ...

Species distribution modeling is a highly versatile tool for understanding the intricate relationship between environmental conditions and species occurrences. However, the available data often lacks information on confirmed species absence and is limited ...

Attribute-based representations help machine learning models perform tasks based on human understandable concepts, allowing a closer human-machine collaboration. However, learning attributes that accurately reflect the content of an image is not always str ...

Social media has created new opportunities to map cultural ecosystem services (CES) related to biodiversity at large scales. However, using these novel data to understand people's preferences in relation to these CES remains a challenge. To address this, w ...

In recent years, several studies have been published about the smart definition of training set using active learning algorithms. However, none of these works consider the contradiction between the active learning methods, which rank the pixels according t ...

The governing hydrological processes are expected to shift under climate change in the alpine regions of Switzerland. This raises the need for more adaptive and accurate methods to estimate river flow. In high-altitude catchments influenced by snow and gla ...

Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered a ...

Peoples’ recreation and well-being are closely related to their aesthetic enjoyment of the landscape. Ecosystem service (ES) assessments record the aesthetic contributions of landscapes to peoples’ well-being in support of sustainable policy goals. However ...

Coconut tree plantations are one of the main sources of income in several South Pacific countries. Thus, keeping track of the location of coconut trees is important for monitoring and post-disaster assessment. Although deep learning based object detectors ...

After a major flood catastrophe, a precious information is the delineation of the affected areas. Remote sensing imagery, especially synthetic aperture radar, allows to obtain a global and complete view of the situation. However, the detection of the flood ...

Example Data for DeepReefMap This dataset contains input videos in MP4 format taken with GoPro Hero 10 Cameras in Reefs in the Red Sea to demonstrate the DeepReefMap tool, which is described in the paper "Scalable Semantic 3D Mapping of Coral Reefs with De ...

OpenStreetMap (OSM) is a community-based, freely available, editable map service created as an alternative to authoritative sources. Given that it is edited mainly by volunteers with different mapping skills, the completeness and quality of its annotations ...

Earth observation (EO) is increasingly used for mapping and monitoring processes occurring at the surface of Earth. Data acquired by satellites nowadays allow us to have a global view, consistent in time, of the state of our forests, oceans, and growing ur ...

In this paper, we tackle the problem of unsupervised classification of hyperspectral images. We propose a clustering method based on graphs representing the data structure, which is assumed to be an union of multiple manifolds. The method constraints the p ...

Antarctica has unique areas that expose blue ice, which contrast to most of the continent (~98%) that is covered by snow. Some of these blue ice areas (BIAs) contain meteorite concentrations and (very) old ice, making them very valuable for understanding o ...

In very high resolution (VHR) image classification it is common to use spatial filters to enhance the discrimination among landuses related to similar spectral properties but different spatial characteristics. However, the filters types that can be used ar ...

Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping. We propose a new regularization method, which enables learning robust classifiers in presence of noisy data. To achieve this goal, we propose ...

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 constructs the dense prediction map based on the similarity between parts o ...

Devis Tuia

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Knowledge-Aware Text–Image Retrieval for Remote Sensing Images

Deep Learning‐based Semantic Segmentation in Remote Sensing

On the selection and effectiveness of pseudo-absences for species distribution modeling with deep learning

Attribute Prediction as Multiple Instance Learning

Social media and deep learning reveal specific cultural preferences for biodiversity

Putting the user into the active learning loop: towards realistic but efficient photointerpretation

Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network

Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences

Social media and deep learning capture the aesthetic quality of the landscape

Towards Efficient Correction of Coconut Tree Detection Errors

Detection of Floods In Sar Images with Non-Linear Kernel Clustering and Topographic Prior

Data for Paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning"

OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing

Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward

Non-linear Low-rank and Sparse Representation for Hyperspectral Image Analysis

Blue ice in Antarctica: The gateway to travel in time and space

Discovering relevant spatial filterbanks for VHR image classification

Wasserstein Adversarial Regularization for learning with label noise

Multi-Scale Grouped Prototypes for Interpretable Semantic Segmentation

Domain adaptation in remote sensing through cross-image synthesis with dictionaries