class: top, left, inverse, title-slide .title[ # Electric Vehicle (EV) Charging Stations Estimation based on Remote Sensing Data ] .author[ ### Lampros Sp. Mouselimis ] .institute[ ### Monopteryx ] .date[ ### 2023-01-13
monopteryx-dashboard/
mlampros.github.io/
] --- class:hide_logo
# EV Charging Stations in EU - A recent special report (“Infrastructure for charging electric vehicles”, 2021) showed that although there are EV charging stations available these are sparse and unevenly distributed making currently travel especially across the European Union (EU) complicated. Infrastructure exists in the central Europe but not in the other EU countries <img src="images/public_charging_points.png" width="60%" style="display: block; margin: auto;" /> --- # Remote Sensing Solution - It is expected that only 3% of EV charging will be done at the gas station in the future. Most of the EV charging will be done, - at home - at work - in parking lots - The developed solution takes this information into consideration and by utilizing - remote sensing data and - machine learning **estimates the number of EV charging stations** within a **resolution** of approximately **6 x 6 square kilometers**. The developed solution incorporates: - Copernicus Sentinel Service Products - NASA Monthly satellite time-series - OpenStreetMap and Geolocation data --- class:hide_logo # Workflow - The following diagram shows the workflow of the developed solution: <img src="images/diagram_page.png" width="120%" height="100%" /> --- class:hide_logo # Results for EU Countries - The *prediction results* - displayed in the next slides - correspond to countries that currently have a *sparse EV-charging station infrastructure* per *100 square kilometers* (land area), such as - *Bulgaria* - *Cyprus* - *Czechia* - *Greece* - *Hungary* - *Malta* - *Poland* - *Romania* - *Slovakia* - Geodata from *Eurostat* was used in **Local Administrative Unit** (LAU) and **Commune** level - For each country a *sample of predictions* is *visualized* (Areas of Interest) and on the right the *correlation* between the *ensemble predictions* and the *Land Cover classes* is included --- # Bulgaria (sample of predictions) .pull-left[ <img src="images/img_predictions/test_BG_OSM.png" width="150%" style="display: block; margin: auto;" /> <br> <img src="images/img_predictions/test_BG_ESRI.png" width="120%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="images/correlation_land_cover/test_BG.png" width="40%" style="display: block; margin: auto 0 auto auto;" /> ] --- # Cyprus (sample of predictions) .pull-left[ <img src="images/img_predictions/test_CY_OSM.png" width="150%" style="display: block; margin: auto;" /> <br> <img src="images/img_predictions/test_CY_ESRI.png" width="120%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="images/correlation_land_cover/test_CY.png" width="40%" style="display: block; margin: auto 0 auto auto;" /> ] --- # Czechia (sample of predictions) .pull-left[ <img src="images/img_predictions/test_CZ_OSM.png" width="150%" style="display: block; margin: auto;" /> <br> <img src="images/img_predictions/test_CZ_ESRI.png" width="120%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="images/correlation_land_cover/test_CZ.png" width="40%" style="display: block; margin: auto 0 auto auto;" /> ] --- # Greece (sample of predictions) .pull-left[ <img src="images/img_predictions/test_EL_OSM.png" width="150%" style="display: block; margin: auto;" /> <br> <img src="images/img_predictions/test_EL_ESRI.png" width="120%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="images/correlation_land_cover/test_EL.png" width="40%" style="display: block; margin: auto 0 auto auto;" /> ] --- # Hungary (sample of predictions) .pull-left[ <img src="images/img_predictions/test_HU_OSM.png" width="150%" style="display: block; margin: auto;" /> <br> <img src="images/img_predictions/test_HU_ESRI.png" width="120%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="images/correlation_land_cover/test_HU.png" width="40%" style="display: block; margin: auto 0 auto auto;" /> ] --- # Malta (sample of predictions) .pull-left[ <img src="images/img_predictions/test_MT_OSM.png" width="150%" style="display: block; margin: auto;" /> <img src="images/img_predictions/test_MT_ESRI.png" width="120%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="images/correlation_land_cover/test_MT.png" width="40%" style="display: block; margin: auto 0 auto auto;" /> ] --- # Poland (sample of predictions) .pull-left[ <img src="images/img_predictions/test_PL_OSM.png" width="150%" style="display: block; margin: auto;" /> <br> <img src="images/img_predictions/test_PL_ESRI.png" width="120%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="images/correlation_land_cover/test_PL.png" width="40%" style="display: block; margin: auto 0 auto auto;" /> ] --- # Romania (sample of predictions) .pull-left[ <img src="images/img_predictions/test_RO_OSM.png" width="150%" style="display: block; margin: auto;" /> <br> <img src="images/img_predictions/test_RO_ESRI.png" width="120%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="images/correlation_land_cover/test_RO.png" width="40%" style="display: block; margin: auto 0 auto auto;" /> ] --- # Slovakia (sample of predictions) .pull-left[ <img src="images/img_predictions/test_SK_OSM.png" width="150%" style="display: block; margin: auto;" /> <br> <img src="images/img_predictions/test_SK_ESRI.png" width="120%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="images/correlation_land_cover/test_SK.png" width="40%" style="display: block; margin: auto 0 auto auto;" /> ] --- # Expertise .pull-left[ <img src="images/r.jpg" width="60%" style="display: block; margin: auto;" /> <img src="images/rstudio.png" width="50%" style="display: block; margin: auto;" /> <img src="images/python.jpg" width="50%" style="display: block; margin: auto;" /> ] .pull-right[ <br><br> - **Expertise** in **data mining** and **extraction** of **valuable information** related to: - Raw satellite imagery - Analysis Ready remote sensing Data - Development of **web browser application prototypes** (using R programming) depending on the user's requirements - **Machine & Deep learning** based either - on *satellite imagery* or - *tabular data* <br> <img src="images/shiny.png" width="30%" style="display: block; margin: auto;" /> ] --- # Pricing The **Pricing** depends on the type of service offered: * For the following **R & Python programming related tasks** potential clients are charged **per hour** * **data & satellite-imagery processing** * **web browser application development** * **machine & deep learning** * For **Data requests** related to pre-processed **EV-charging predictions** potential clients are charged based on * the **Number of requested Hexagons** where each Hexagon corresponds to a **spatial pixel** (approximately **6 x 6 square kilometers**) * the estimated number (predicted) **EV-charging stations** [high vs. low predicted values per hexagon] Feel free to reach out and request a quote by filling out the [Inquiry Form](https://monopteryx.netlify.app/contact/) <br /> in the following weblink: [https://monopteryx.netlify.app/contact/](https://monopteryx.netlify.app/contact/)