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Found 14 papers, 9 papers with code
Fully Convolutional Siamese Networks for Change Detection
This paper presents three fully convolutional neural network architectures which perform change detection using a pair of coregistered images.
Ranked #1 on
Change Detection
on OSCD - 13ch
Change Detection
Change detection for remote sensing images
+1
Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks
The Copernicus Sentinel-2 program now provides multispectral images at a global scale with a high revisit rate.
Multitask Learning for Large-scale Semantic Change Detection
In this paper we present the first large scale high resolution semantic change detection (HRSCD) dataset, which enables the usage of deep learning methods for semantic change detection.
Guided Anisotropic Diffusion and Iterative Learning for Weakly Supervised Change Detection
Large scale datasets created from user labels or openly available data have become crucial to provide training data for large scale learning algorithms.
Weakly Supervised Change Detection Using Guided Anisotropic Difusion
Large scale datasets created from crowdsourced labels or openly available data have become crucial to provide training data for large scale learning algorithms.
Urban Change Forecasting from Satellite Images
In Stage 1, a U-Net backbone is pretrained within a Siamese network architecture that aims to solve a (building) change detection task.
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation
We show that the idea can be extended to uncertainty quantification: by modulating the network activations of a single deep network with FiLM, one obtains a model ensemble with high diversity, and consequently well-calibrated estimates of epistemic uncertainty, with low computational overhead in comparison.
Recognition of Unseen Bird Species by Learning from Field Guides
Illustrations contained in field guides deliberately focus on discriminative properties of each species, and can serve as side information to transfer knowledge from seen to unseen bird species.
Satellite-based high-resolution maps of cocoa planted area for Côte d'Ivoire and Ghana
C\^ote d'Ivoire and Ghana, the world's largest producers of cocoa, account for two thirds of the global cocoa production.
Guided Depth Super-Resolution by Deep Anisotropic Diffusion
In this work, we propose a novel approach which combines guided anisotropic diffusion with a deep convolutional network and advances the state of the art for guided depth super-resolution.
BiasBed - Rigorous Texture Bias Evaluation
With the release of BiasBed, we hope to foster a common understanding of consistent and meaningful comparisons, and consequently faster progress towards learning methods free of texture bias.
High-resolution Population Maps Derived from Sentinel-1 and Sentinel-2
With our work we aim to democratize access to up-to-date and high-resolution population maps, recognizing that some regions faced with particularly strong population dynamics may lack the resources for costly micro-census campaigns.
Neural Fields with Thermal Activations for Arbitrary-Scale Super-Resolution
We present a novel way to design neural fields such that points can be queried with an adaptive Gaussian PSF, so as to guarantee correct anti-aliasing at any desired output resolution.
Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
Monocular depth estimation is a fundamental computer vision task.
Ranked #6 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
