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Found 13 papers, 6 papers with code
Country-Scale Cropland Mapping in Data-Scarce Settings Using Deep Learning: A Case Study of Nigeria
In this study, we explore the usefulness of combining a global cropland dataset and a hand-labeled dataset to train machine learning models for generating a new cropland map for Nigeria in 2020 at 10 m resolution.
Self-supervised learning unveils change in urban housing from street-level images
Cities around the world face a critical shortage of affordable and decent housing.
What You See is What You Classify: Black Box Attributions
These attributions are provided in the form of masks that only show the classifier-relevant parts of an image, masking out the rest.
Using Machine Learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan Region
To this end, we developed an ML pipeline that relies on Sentinel-2 satellite images time series.
Semisupervised Manifold Alignment of Multimodal Remote Sensing Images
We introduce a method for manifold alignment of different modalities (or domains) of remote sensing images.
A survey of active learning algorithms for supervised remote sensing image classification
Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines.
Decision fusion with multiple spatial supports by conditional random fields
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.
Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images
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).
Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models
In remote sensing images, the absolute orientation of objects is arbitrary.
Detecting animals in African Savanna with UAVs and the crowds
Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife monitoring, with several advantages over traditional field-based methods.
Rotation equivariant vector field networks
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
Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks
In this paper we present a CNN-based system relying on an downsample-then-upsample architecture.
Learning rotation invariant convolutional filters for texture classification
We present a method for learning discriminative filters using a shallow Convolutional Neural Network (CNN).
