Search Results for author: Nando Metzger

Found 8 papers, 5 papers with code

Neural Fields with Thermal Activations for Arbitrary-Scale Super-Resolution

1 code implementation29 Nov 2023 Alexander Becker, Rodrigo Caye Daudt, Nando Metzger, Jan Dirk Wegner, Konrad Schindler

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.

Image Super-Resolution

High-resolution Population Maps Derived from Sentinel-1 and Sentinel-2

no code implementations23 Nov 2023 Nando Metzger, Rodrigo Caye Daudt, Devis Tuia, Konrad Schindler

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.

Humanitarian Population Mapping

Guided Depth Super-Resolution by Deep Anisotropic Diffusion

1 code implementation CVPR 2023 Nando Metzger, Rodrigo Caye Daudt, Konrad Schindler

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.


Urban Change Forecasting from Satellite Images

no code implementations27 Apr 2022 Nando Metzger, Mehmet Özgür Türkoglu, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler

In Stage 1, a U-Net backbone is pretrained within a Siamese network architecture that aims to solve a (building) change detection task.

Change Detection Management

DSM Refinement with Deep Encoder-Decoder Networks

no code implementations14 Dec 2020 Nando Metzger

However, the calculated DSMs suffer from noise, artefacts, and data holes that have to be manually cleaned up in a time-consuming process.

Image Classification

Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations

1 code implementation4 Dec 2020 Nando Metzger, Mehmet Ozgur Turkoglu, Stefano D'Aronco, Jan Dirk Wegner, Konrad Schindler

We propose to use neural ordinary differential equations (NODEs) in combination with RNNs to classify crop types in irregularly spaced image sequences.

Crop Classification Earth Observation +2

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