558 citations · 2k across the 15 of their papers we have counts for
16 papers
Learning to Forecast Crop Growth from Earth Observation Data
Dominik Senti, Mehmet Ozgur Turkoglu, Michele Volpi +1
Forecasting crop growth across agricultural landscapes is important for improving the productivity, resilience, and operational management of farming systems. In this work, we inve…
GrowFields: Compositional 4D Neural Fields for Topology-Changing Plant Growth
Joaquin Gajardo, Michele Volpi, Marko Mihajlovic +3
Quantifying plant growth dynamics from sparse longitudinal 3D observations is fundamental for agriculture and plant sciences. Yet, plants pose unique challenges: they undergo intri…
Country-wide, high-resolution monitoring of forest browning with Sentinel-2
Samantha Biegel, David Brüggemann, Francesco Grossi +3
Natural and anthropogenic disturbances are impacting the health of forests worldwide. Monitoring forest disturbances at scale is important to inform conservation efforts. Here, we…
Evaluating the Role of Training Data Origin for Country-Scale Cropland Mapping in Data-Scarce Regions: A Case Study of Nigeria
Joaquin Gajardo, Michele Volpi, Daniel Onwude +1
Cropland maps are essential for remote sensing-based agricultural monitoring, providing timely insights without extensive field surveys. Machine learning enables large-scale mappin…
Self-supervised learning unveils change in urban housing from street-level images
Steven Stalder, Michele Volpi, Nicolas Büttner +3
Cities around the world face a critical shortage of affordable and decent housing. Despite its critical importance for policy, our ability to effectively monitor and track progress…
What You See is What You Classify: Black Box Attributions
Steven Stalder, Nathanaël Perraudin, Radhakrishna Achanta +2
An important step towards explaining deep image classifiers lies in the identification of image regions that contribute to individual class scores in the model's output. However, d…
Using Machine Learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan Region
Danya Li, Joaquin Gajardo, Michele Volpi +1
Crop maps are crucial for agricultural monitoring and food management and can additionally support domain-specific applications, such as setting cold supply chain infrastructure in…
Semisupervised Manifold Alignment of Multimodal Remote Sensing Images
Devis Tuia, Michele Volpi, Maxime Trolliet +1
We introduce a method for manifold alignment of different modalities (or domains) of remote sensing images. The problem is recurrent when a set of multitemporal, multisource, multi…
A survey of active learning algorithms for supervised remote sensing image classification
Devis Tuia, Michele Volpi, Loris Copa +2
Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited te…
Decision fusion with multiple spatial supports by conditional random fields
Devis Tuia, Michele Volpi, Gabriele Moser
Classification of remotely sensed images into land cover or land use is highly dependent on geographical information at least at two levels. First, land cover classes are observed…
Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images
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…
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…
Detecting animals in African Savanna with UAVs and the crowds
Nicolas Rey, Michele Volpi, Stéphane Joost +1
Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife monitoring, with several advantages over traditional field-based methods. They have readily been used to count…
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…
Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks
Michele Volpi, Devis Tuia
Semantic labeling (or pixel-level land-cover classification) in ultra-high resolution imagery (< 10cm) requires statistical models able to learn high level concepts from spatial da…
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…