Miguel Neto

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Universidade de Aveiro

Portugal

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Recent Grants

Grant: Close

C-Tech - Climate Driven Technologies for Low Carbon Cities

Open Date: 2020-05-01

Close Date:

Grant: Close

Urban Co-Creation Data Lab

Open Date: 2019-10-01

Close Date:

Grant: Close

AESBIO - Assessment of Ecosystem Services, Biodiversity, and Well-Being in Portugal

Open Date: 2018-01-01

Close Date: 2021-01-01

Grant: Close

Projecto GEONATURA Open educational resources platform for Geomatics applications to social and environmental issues

Open Date: 2017-01-01

Close Date: 2020-01-01

Grant: Close

SIM4SECURITY

Open Date: 2014-01-01

Close Date:

Articles (20)

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Universidade de Aveiro

PORTUGAL

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Miguel Neto

Research Interests

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Recent Grants

Articles (20)

Lisa

Assessing the risk of traffic accidents in lisbon using a gradient boosting algorithm with a hybrid classification/regression approach

Predicting key touchpoints in hotel customer journey

An indicator for integrated regional planning

The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities

Fisheries Inspection in Portuguese Waters from 2015 to 2023

Long-term in situ Eulerian Sea surface temperature records along the Portuguese Coast

Side-scan sonar imaging data of underwater vehicles for mine detection

A Spatiotemporal Comparative Analysis of Docked and Dockless Shared Micromobility Services

Leveraging technology for waste sustainability

Georeferenced dataset of maritime piracy in the Gulf of Guinea from 2010 to 2021

Impact of COVID-19 lockdown measures on waste production behavior in Lisbon

Beyond comorbidities, sex and age have no effect on COVID-19 health care demand

Using NDVI, climate data and machine learning to estimate yield in the Douro wine region

The Illegal Parking Score

Walkability Indicators in the Aftermath of the COVID-19 Pandemic

Adoption of new household waste management technologies

The Daily Urban Dynamic Indicator

Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review

A mixed approach for urban flood prediction using Machine Learning and GIS

Collaborators (9)

Tiago Oliveira

Jorge M. Mendes

Ricardo Moura

André Barriguinha

Ian Scott

Flávio Pinheiro

Manuela Aparicio

André Oliveira

Victor Lobo

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