Science Journal graduates from Google to Arduino

Science Journal is an open source mobile app that enables students in K-12 classrooms to conduct fun, hands-on science experiments using smart devices. Since its launch in May 2016, Science Journal has gone through quite a journey, from collaborating with rock stars to supporting classrooms, through an integration with Google Drive. Now we're pleased to share that Science Journal has graduated from Google and moved over to Arduino, the makers of the popular open source Arduino microcontrollers for students, hobbyists, and professionals around the world. Arduino Science Journal is free, open sourced, and available for download today on Android and iOS. 

We're thrilled to be handing the project to a partner that has a long history of supporting open source and education. The Arduino Science Kit for middle school students was developed in 2019 in partnership with Science Journal. The Arduino Science Journal Android source code and iOS source code, are already available on GitHub along with the Science Journal Arduino firmware. We've put a lot of time and energy into making Science Journal a great app for students and science enthusiasts everywhere, and we're confident that it will continue to thrive in its new home.

Although Google's Science Journal apps are still available on the App and Play Store today, these apps will no longer be supported after December 11, 2020, at which point Google Drive Syncing will stop working and Google's versions of the apps will no longer be available for download. However, existing Science Journal experiments can be exported from Google's Science Journal apps and imported into Arduino Science Journal at any time. You can find more information about this handoff in our Help Center article.

We see this change as a win for Google, Arduino, Science Journal, and for open source overall. Since Science Journal is an app for kids and schools, we wanted to be particularly careful with this transition. By supporting Arduino in releasing their own version of Science Journal and forking our code on GitHub, we were able to effectively hand off the project without transferring any user data or Intellectual Property. We hope this approach can serve as an effective model for future projects that need to reallocate their resources but don't want to let down their users (as we like to say: focus on the user, and all else will follow).

Moving forward, all future updates will be happening through Arduino's versions of the apps. You can stay up-to-date on the Arduino Science Journal website and experiment with their new hands-on activities, and if you have any questions, you can contact them on the Arduino Science Journal Forum.

Although the Science Journal project is moving on from Google, we still think data and scientific literacy are critically important for present and future generations, now more than ever. With the ubiquity of smart devices in classrooms and at home, we think Science Journal remains the perfect solution for parents and teachers looking to provide students with hands-on learning opportunities during this time period. We hope you enjoy using Science Journal as much as we have, and we're excited to see how the project will continue to evolve moving forward.

By Maia Deutsch and the Science Journal Team

Recreating historical streetscapes using deep learning and crowdsourcing

For many, gazing at an old photo of a city can evoke feelings of both nostalgia and wonder. We have Google Street View for places in the present day, but what about places in the past? What was it like to walk through Manhattan in the 1940s? To create a rewarding “time travel” experience for both research and entertainment purposes, Google Research is launching Kartta Labs, an open source, scalable system on Google Cloud and Kubernetes that tackles the difficult problem of reconstructing what cities looked like in the past from scarce historical maps and photos.

Kartta Labs consists of three main parts:

• A temporal map server, which shows how maps change over time;
• A crowdsourcing platform, which allows users to upload historical maps of cities, georectify, and vectorize them (i.e. match them to real world coordinates);
• And an upcoming 3D experience platform, which runs on top of maps creating the 3D experience by using deep learning to reconstruct buildings in 3D from limited historical images and maps data.

Maps & Crowdsourcing

Kartta Labs is a growing suite of open source tools that work together to create a map server with a time dimension, allowing users to populate the service with historically accurate data.

Warper

The entry point to crowdsourcing is Warper, an open source web app based on MapWarper that allows users to upload historical images of maps and georectify them by finding control points on the historical map and corresponding points on a base map.

Once a user uploads a scanned historical map, Warper makes a best guess of the map’s geolocation by extracting textual information from the map. This initial guess is used to place the map roughly in its location and allow the user to georeference the map pixels by placing pairs of control points on the historical map and a reference map. Given the georeferenced points, the application warps the image such that it aligns well with the reference map.

Warper runs as a Ruby on Rails application using a number of open source geospatial libraries and technologies, including but not limited to PostGIS and GDAL. The resulting maps can be exported in PNG, GeoTIFF, and other open formats. Warper also runs a raster tiles server that serves each georectified map at a tile URL. This raster tile server is used to load the georectified map as a background in the Editor application that is described next.

Editor

Editor is an open source web application which is a customized version of the OpenStreetMap editor; customizations include support for time dimension and integration with the other tools in the Kartta Labs suite. Editor allows users to load the georectified historical maps and trace their geographic features (e.g., building footprints, roads, etc.). This traced data is stored in vector format.

Extracted geometries in vector format, as well as metadata (e.g., address, name, and start or end dates), are stored in a geospatial database that can be queried, edited, styled, and rendered into new maps.

Kartta

Finally, the temporal map front end, Kartta (based on Tegola), visualizes the vector tiles allowing the users to navigate historical maps in space and time. Kartta works like any familiar map application (such as Google Maps), but also has a time slider so the user can choose the year at which they want to see the map. By moving the time slider, the user is able to see how features in the map, such as buildings and roads, changes over time.

3D Experience

To actually create the “time traveling” 3D experience, the forthcoming 3D Models module aims to reconstruct the detailed full 3D structures of historical buildings. The module will associate images with maps data, organize these 3D models properly in one repository, and render them on the historical maps with a time dimension.

Preliminary Results

Figure 2 – Bird’s eye view of 3D-reconstructed  Chelsea, Manhattan with a time slider

Figure 3 – Street level view of 3D-reconstructed Chelsea, Manhattan

Conclusion

We developed the tools outlined above to facilitate crowdsourcing and tackle the main challenge of insufficient historical data. We hope Kartta Labs acts as a nexus for an active community of developers, map enthusiasts, and casual users that not only utilizes our historical datasets and open source code, but actively contributes to both. The launch of our implementation of the Kartta Labs suite is imminent—keep an eye out on the Google AI blog for that announcement!

By Raimondas Kiveris – Google Research

Google Summer of Code 2020: Learning Together

In its 16th year of the program, we are pleased to announce that 1,106 students from 65 countries have successfully completed Google Summer of Code (GSoC) 2020! These student projects are the result of three months of collaboration between students, 198 open source organizations, and over 2,000 mentors from 67 countries.

During the course of the program what we learned was most important to the students was the ability to learn, mentorship, and community building. From the student evaluations at the completion of the program, we collected additional statistics from students about the GSoC program, where we found some common themes. The word cloud below shows what mattered the most to our students, and the larger the word in the cloud, the more frequently it was used to describe mentors and open source.

Valuable insights collected from the students:

• 94% of students think that GSoC helped their programming
• 96% of students would recommend their GSoC mentors
• 94% of students will continue working with their GSoC organization
• 97% of students will continue working on open source
• 27% of students said GSoC has already helped them get a job or internship

The GSoC program has been an invaluable learning journey for students. In tackling real world, real time implementations, they've grown their skills and confidence by leaps and bounds. With the support and guidance from mentors, they’ve also discovered that the value of their work isn’t just for the project at hand, but for the community at large. As newfound contributors, they leave the GSoC program enriched and eager to continue their open source journey.

Throughout its 16 years, GSoC continues to ignite students to carry on their work and dedication to open source, even after their time with the program has ended. In the years to come, we look forward to many of this year’s students paying it forward by mentoring new contributors to their communities or even starting their own open source project. Such lasting impact cannot be achieved without the inspiring work of mentors and organization administrators. Thank you all and congratulations on such a memorable year!

By Romina Vicente, Project Coordinator for the Google Open Source Programs Office