## Inspiration

Growing up in the early 2000s, communiplant's founding team knew what it was like to grow up in vibrant communities, interconnected interpersonal and naturally.

Today's post-covid fragmented society lacks the community and optimism that kept us going. The lack of optimism is especially evident through our climate crisis: an issue that falls outside most individuals loci of control.

That said, we owe it to ourselves and future generations to keep hope for a better future alive, **and that future starts on the communal level**. Here at Communiplant, we hope to help communities realize the beauty of street-level biodiversity, shepherding the optimism needed for a brighter future.

## What it does

Communiplant allows community members to engage with their community while realizing their jurisdiction's potential for sustainable development. Firstly, the communiplant analyzes satellite imagery using machine learning and computer vision models to calculate the community's NDMI vegetation indices. Beyond that, community members can individually contribute to their community on Commuiplant by uploading images of various flora and fauna they see daily in their community. Using computer vision models, our system can label the plantlife uploaded to the system, serving as a mosaic representing the communities biodiversity.

Finally, to further engage with their communities, users can participate in the community through participation in a variety of community events.

## How we built it

Communitech is a fullstack web application developed using React & Vite for the frontend, and Django on the backend. We used AWS's cloud suite for relational data storage: storing user records. Beyond that, however, we used AWS to implement the algorithms necessary for the complex categorizations that we needed to make. Namely. we used AWS S3 object storage to maintain our various clusters.

Finally, we used a variety of browser-level apis, including but not limited to the google maps API and the google earth engine API.

## Challenges we ran into

While UOttahack6 has been incredibly rewarding, it has not been without it challenges. Namely, we found that attempting to use bleeding-edge new technologies that we had little experience with in conjunction led to a host of technical issues.

First and most significantly, we found it difficult implementing cloud based artificial intelligence workflows for the first time.

We also had a lot of issues with some of the browser-level maps APIs, as we found that the documentation for some of those resources was insufficient for our experience level.

## Accomplishments that we're proud of

Regardless of the final result, we are happy to have made a final product with a concrete use case that has potential to become major player in the sustainability space.

All in all however, we are mainly proud that through it all we were able to show technical resilience. There were many late night moments where we didn't really see a way out, or where we would have to cut out a significant amount of functionality from our final product. Regardless we pushed though, and those experiences are what we will end up remembering UOttahack for.

## What's next for Communiplant

The future is bright for Communplant with many features on the way. Of these, the most significant are related to the mapping functionality. Currently, user inputted flora and fauna live only in a photo album on the community page. Going forwards we hope to have images linked to geographic points, or pins on the map.

Regardless of Communiplant's future actions, however, we will keep our guarantee to support sustainability on all scales.
## Inspiration

As college students learning to be socially responsible global citizens, we realized that it's important for all community members to feel a sense of ownership, responsibility, and equal access toward shared public spaces. Often, our interactions with public spaces inspire us to take action to help others in the community by initiating improvements and bringing up issues that need fixing. However, these issues don't always get addressed efficiently, in a way that empowers citizens to continue feeling that sense of ownership, or sometimes even at all! So, we devised a way to help FixIt for them!

## What it does

Our app provides a way for users to report Issues in their communities with the click of a button. They can also vote on existing Issues that they want Fixed! This crowdsourcing platform leverages the power of collective individuals to raise awareness and improve public spaces by demonstrating a collective effort for change to the individuals responsible for enacting it. For example, city officials who hear in passing that a broken faucet in a public park restroom needs fixing might not perceive a significant sense of urgency to initiate repairs, but they would get a different picture when 50+ individuals want them to FixIt now!

## How we built it

We started out by brainstorming use cases for our app and and discussing the populations we want to target with our app. Next, we discussed the main features of the app that we needed to ensure full functionality to serve these populations. We collectively decided to use Android Studio to build an Android app and use the Google Maps API to have an interactive map display.

## Challenges we ran into

Our team had little to no exposure to Android SDK before so we experienced a steep learning curve while developing a functional prototype in 36 hours. The Google Maps API took a lot of patience for us to get working and figuring out certain UI elements. We are very happy with our end result and all the skills we learned in 36 hours!

## Accomplishments that we're proud of

We are most proud of what we learned, how we grew as designers and programmers, and what we built with limited experience! As we were designing this app, we not only learned more about app design and technical expertise with the Google Maps API, but we also explored our roles as engineers that are also citizens. Empathizing with our user group showed us a clear way to lay out the key features of the app that we wanted to build and helped us create an efficient design and clear display.

## What we learned

As we mentioned above, this project helped us learn more about the design process, Android Studio, the Google Maps API, and also what it means to be a global citizen who wants to actively participate in the community! The technical skills we gained put us in an excellent position to continue growing!

## What's next for FixIt

An Issue’s Perspective

 \* Progress bar, fancier rating system

 \* Crowdfunding

A Finder’s Perspective

 \* Filter Issues, badges/incentive system

A Fixer’s Perspective

 \* Filter Issues off scores, Trending Issues
## Inspiration

As children some of us have been to the great barrier reef and it was an amazing experience. Now with the problems such as climate change and pollution that have effected these natural wonders, we were inspired to help people maintain land better in general. There has not been much advancements in the ways of keep an eye on our land considering the advancements in technology so we have come up with ideas of how we can use satellite images and to run models on these images in order to get information that you would not be able to otherwise know in order to take the best care possible of the land.

## What it does

Our application takes in coordinates of a location through an interactive Google Maps environment and will run models on that area of land in order to analyze it, reporting various information such as water index, vegetation index, normalized vegetation index, burn area index, mosture index, mineral and clay indices, and land coverage segmentation.

## How we built it

We built this with a reactJS frontend and an express server backend. Our backend talks to our python analytical model that handle the indices and land coverage prediction. Please see the component diagram above.

## Challenges we ran into

Dataset usability was one big issue. We found that although there was many good datasets out there, not all of them were usable, rendering them useless. There was challenges with some of the libraries that we were working with. Not everyone was able to download the libraries and work with them due to difference in computer architectures and different operating systems. We were able to work around this by using the Git versioning system.

## Accomplishments that we're proud of

Able to get good images of indices that do vegetation, moisture levels, and high resolution RGB maps for land cover segmentation

## What we learned

There are a lot of cool datasets that we can work with in order to accomplish these tasks. These objectives can be accomplish through many different means.

## What's next for EnviroHack

We plan on expanding on the idea of this project by adding drones that will have the capability of streaming the images of the land rather than using satellites. This gives the user more real-time updates of the area of interest. Furthermore, we want to be able to use the drone to accomplish some tasks such as planting trees and cleaning trash in order to save take care of the land autonomously.