HasiruAqua is a startup that is committed to providing next-generation technology to the agriculture and aquaculture ecosystem under low-cost or affordable business models that enrich the lives of farmers and consumers. Our two-fold mission is to improve the lives of farmers through economic transformation and to improve the quality of nutrition that citizens consume.
We’ve been working on revolutionizing the way of cultivating and consuming fish.Current practices of harvesting from natural sources are not only causing severe ecological damage but are also unsustainable and leading to reduced supply and increased pricing. HasiruAqua cultivates safe, hygienic fish by partnering with farmers and using technology. We offer farmers a “Full-Stack Fish Farming” service that helps them through the entire cultivation journey. We sell this produce to distributors, exporters and D2C brands at competitive rates and share profits with the farmers. Thus we are able to create an endless supply of farmed fish and help the farmer increase his annual income.
Integrating open mapping tools we have mapped 20k ponds which can help grow 20,000 tons of fish and other aquaculture products per year and increase the farmer income by 45% year on year. Combining Open Street Mapping tools and Hasiru Aqua technology data we can provide Anticipatory Action for Disaster Mitigation for farmers which will help reduce the risk of losing produce.
Full stack web mapping workshop includes each and every component of Web Mapping. It will mainly focus on the backend development of Map API, Routing API & Geocoding API using the Openstreetmap. The workshop will be divided into 5 modules
Module -1: Introduction to Web GIS
This module will focus on the basics of web GIS and Enterprise & Open Source Web GIS solutions
Module -2: Development Tools
This module will focus on different types of web development tools, Postgresql DB, PostGIS, DB tools, and a few other web GIS development tools
Module -3: Map Server
This module will focus on the Map Servers. A few well-known map servers will be discussed but mainly Tileserver-GL engine will be developed and tested through a live server from scratch in the hands-on segment.
Module -4: Routing Engine
This module will focus on three types of routing engines. In the hands-on segment, OSRM will be installed and tested through a live server.
Module -5: Geocoding Engine
This module will focus on the two types of Geocoding Engine. In the hands-on segment, Pelias will be installed and tested through a live server
The following individual topics will be covered in high level
3. Tileserver GL
Learners will get hands-on experience using OpenStreetMap data for disaster resilience mapping during this 2-hour course. The talk will begin with a brief overview of OpenStreetMap and how this free and open-source geospatial dataset is being used in many impact areas throughout the world. They would subsequently be given the opportunity to brush up on their knowledge of the QGIS interface. Following that, the QGIS plugins will be introduced, and learners will be able to test them out using OpenStreetMap sample datasets. The ultimate goal is to assist learners in understanding the use of open mapping data and tools like OSM and QGIS in various stages of disaster management.
Urbanization presents opportunities and challenges, with slum areas being a critical concern in many rapidly growing cities. Accurate identification and prediction of slum areas are imperative for effective urban planning and resource allocation. We propose using OpenStreetMap (OSM) Building data to identify slum areas in regions where slum datasets are lacking. The proposed methodology incorporates variable extraction using the “momepy” library to identify and characterize buildings effectively.
In this study, we employ a two-stage process. Firstly, we utilize the momepy library to extract pertinent variables that encapsulate geometric and topological attributes of buildings from OSM data. These variables serve as inputs to our machine-learning model. Secondly, a predictive model is developed using machine learning algorithms for regions with known slum areas. This model is then employed to predict potential slum areas in regions with insufficient or lacking slum-related data.
Preliminary experiments conducted on real-world datasets demonstrate the effectiveness of our approach. The results underscore the utility of OSM data in slum area detection, showcasing its capacity to bridge data gaps and facilitate proactive urban planning. Furthermore, the proposed approach offers adaptability to various urban contexts, making it a valuable tool for decision-makers and researchers in diverse regions. Additionally, the study contributes to ongoing discussions regarding the reliability of OSM data, especially in challenging urban environments like slums.
KoboToolbox is an open-source data collection, management, and visualization platform that is used by thousands of organizations worldwide in their data needs. It is extremely user friendly and accessible, making it easy to get started quickly. It works offline particularly through its mobile application counterpart, KoboCollect.
This workshop aims to teach how to utilize KoboToolbox for collecting and managing data and will cover the following topics: (1) Creating survey forms with KoboToolbox Form Builder; (2) Mobile field data collection with KoboCollect; (3) Data validation and analysis; and (4) Project and team management.
The “Battle of the Best OSM Mobile Data Collection Tool” is a workshop that aims to delve into two cutting-edge mobile data collection tools for OpenStreetMap: Organic Maps and EveryDoor. This workshop will serve as a platform for participants to gain insights, hands-on experience, and a comprehensive understanding of these innovative approaches to OSM data enrichment. By the end of the workshop, participants will not only grasp the technical intricacies but also understand the overarching societal and technological impacts of enhanced OSM data collection.
The workshop would feature the usage of two OSM mobile data collection tools, Organic Maps and EveryDoor. The functions of the said tools will be discussed then an actual utilization of them will be performed by the workshop participants. The participants are expected to share their experiences in testing the tools.
The YouthMappers Regional Validation Hub Formulation aims to establish a centralized hub for validating mapping data generated by YouthMappers chapters in the region. This is to ensure the accuracy and quality of the mapping outputs before they are endorsed to the YouthMappers Global Validation Hub.
The Regional Validation Hub Formulation emphasizes the importance of maintaining data integrity and quality within the YouthMappers network. By establishing a regional hub, YouthMappers in Asia can benefit from a centralized platform for validation, ensuring that their mapping efforts contribute to reliable and impactful geospatial data.
The purpose of this workshop is to teach the validation tools of JOSM to the prospective validators in Asia-Pacific. The workshop will cover the following topics: configuring JOSM, fixing common errors and warnings, and getting feedback on proper validation techniques.
In geospatial analysis, OpenStreetMap (OSM) data have become essential for urban planning, environmental assessment, and location-based services. However, these tasks often require a complex interplay between human expertise and technical tools. To bridge this gap, we present OSM-GPT, a groundbreaking opensource tool that seamlessly integrates artificial intelligence, particularly the ChatGPT API, into the geospatial domain. This fusion enables users to effortlessly generate OSM queries, extract OSM data, and visualize it in a custom and interactive manner.
## Key Features:
**AI-Powered Query Generation:**
OSM-GPT’s hallmark feature is its ability to generate OSM queries via the ChatGPT API. Users can simply describe their requirements, such as “get all buildings” or “extract road networks”, and OSM-GPT will transform these descriptions into actionable queries.
**Effortless Data Extraction:**
Overpass API is used to retrieve OSM data using the queries generated by OSM-GPT. Each query generates a dedicated layer, simplifying data management and enhancing the visualization experience.
**Manual Query Support:**
While AI-generated queries are a highlight, OSM-GPT also caters to users familiar with OSM query syntax. This enables the manual input of queries for extracting specific OSM data, ensuring flexibility and catering to a wide range of user expertise levels.
**Custom Layer Creation:**
To offer a comprehensive view of the data, OSM-GPT empowers users to customize layer colors. This characteristic improves the distinction of data and assists in effectively conveying valuable insights.
**Selective GeoJSON Downloads:**
OSM-GPT enhances data utilization by allowing users to download GeoJSON files tailored to their needs. Users can choose to download only polygons, lines, points, or all features.
## Future Prospects
OSM-GPT’s roadmap includes plans to introduce custom layer overlay functionality. The integration of users’ own spatial data with OSM extracts will facilitate dynamic and insightful visualizations.
## Use Case Scenario:
In times of disaster, OSM-GPT can be a lifesaver. It quickly figures out where buildings, roads, and hospitals are in the affected area without volunteers spending time searching. These key details are then displayed on a map, helping responders decide where to send help and where there are clear roads. Volunteers don’t need to worry about complicated queries; they can focus on taking action.
OSM-GPT marks a groundbreaking advancement in making geospatial discovery and analysis accessible to a wider audience. By integrating AI capabilities with OSM data manipulation, the tool streamlines the process of query generation, data extraction, visualization, and customization. It empowers users of varying technical backgrounds to harness the power of geospatial data and transform it into actionable insights.
Source Code: [https://github.com/rowheat02/osm-gpt](https://github.com/rowheat02/osm-gpt)
Try OSM-GPT: [https://dub.sh/try-osm-gpt](https://dub.sh/try-osm-gpt)