Search Results for author: Marta Gonzalez

Found 7 papers, 3 papers with code

Using machine learning to understand causal relationships between urban form and travel CO2 emissions across continents

1 code implementation31 Aug 2023 Felix Wagner, Florian Nachtigall, Lukas Franken, Nikola Milojevic-Dupont, Rafael H. M. Pereira, Nicolas Koch, Jakob Runge, Marta Gonzalez, Felix Creutzig

Here, we address all three gaps via causal graph discovery and explainable machine learning to detect urban form effects on intra-city car travel, based on mobility data of six cities across three continents.

Causal Discovery Specificity

High contrast and resolution near infrared photometry of the core of R136

no code implementations11 Feb 2021 Zeinab Khorrami, Maud Langlois, Paul C. Clark, Farrokh Vakili, Anne S. M. Buckner, Marta Gonzalez, Paul Crowther, Richard Wunsch, Jan Palous, Stuart Lumsden, Estelle Moraux

Our aim was to (i) increase the number of resolved sources in the core of R136, and (ii) to compare with the first epoch to classify the properties of the detected common sources between the two epochs.

Solar and Stellar Astrophysics Astrophysics of Galaxies

Streetify: Using Street View Imagery And Deep Learning For Urban Streets Development

no code implementations18 Nov 2019 Fahad Alhasoun, Marta Gonzalez

In this paper, we propose an approach to collect and label imagery data then deploy advancements in computer vision towards modern urban planning.

Classification General Classification

Spatial sensitivity analysis for urban land use prediction with physics-constrained conditional generative adversarial networks

no code implementations22 Jul 2019 Adrian Albert, Jasleen Kaur, Emanuele Strano, Marta Gonzalez

Accurately forecasting urban development and its environmental and climate impacts critically depends on realistic models of the spatial structure of the built environment, and of its dependence on key factors such as population and economic development.

Image-to-Image Translation

Modeling urbanization patterns with generative adversarial networks

1 code implementation8 Jan 2018 Adrian Albert, Emanuele Strano, Jasleen Kaur, Marta Gonzalez

In this study we propose a new method to simulate hyper-realistic urban patterns using Generative Adversarial Networks trained with a global urban land-use inventory.

Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale

3 code implementations10 Apr 2017 Adrian Albert, Jasleen Kaur, Marta Gonzalez

For supervision, given the limited availability of standard benchmarks for remote-sensing data, we obtain ground truth land use class labels carefully sampled from open-source surveys, in particular the Urban Atlas land classification dataset of $20$ land use classes across $~300$ European cities.

General Classification Image Classification

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