The EASIER Data Initiative#
Efficient, Accessible, and Sustainable Infrastructure for Extracting Reliable (EASIER) Data for the World’s Location-Based Assets
Partners#
Updates#
2024-12-06 - Revolutionizing Farming with Decentralized Networks and Multi-Temporal Crop Classification Modeling
Ever wondered how satellites help us understand what’s growing on our planet? In a world facing population growth, erratic weather, and food security challenges, crop classification has become a crucial tool for modern agriculture. Using satellite data to identify and track different types of crops, we can deliver critical analyses of global agriculture information. This post’ll dive into how NASA’s Harmonized Landsat and Sentinel-2 (HLS) data and the USDA’s Cropland Data Layer (CDL) are used in a multi-temporal crop classification pipeline. We’ll explore the use of decentralized networks such as IPFS and Filecoin and how they offer a more resilient and accessible approach to agricultural analysis and decision-making processes. Whether you’re new to crop classification or just curious about how these systems work together to forecast yields and inform agricultural decisions, this post will give you a perspective into the process and its potential impact on global food security.
2024-11-04 - Changes in ipfs-stac v0.2.0
One of the pivotal projects the EASIER Data Initiative has produced is ipfs-stac. The Python library is a testament to the feasibility of onboarding and interfacing geospatial data on IPFS. The library enables developers and researchers to leverage STAC APIs enriched with Filecoin and IPFS metadata to seamlessly fetch, pin, and explore data in a familiar manner. In an ambiguous ecosystem with everchanging advancements, updates, breaking changes, and new features of the infrastructure will emerge. The team has made it a responsibility to adhere to these changes and that has prompted our projects to remain flexible. This notebook will explore the many new features and changes to ipfs-stac in version 0.2. To stay up to date with the development and release of new features, consider starring our respository for ipfs-stac
2024-10-28 - GEDI - The Force Awakens in Open Science
Measuring the impact of deforestation is one of the many overarching goals of the Global Ecosystem Dynamics Investigation (GEDI) project. With support of the GEDI’s LIDAR system and hitching a ride on the International Space Station, high-quality laser ranging observations collect a 3D structure of the Earths forests for which can be used to estimate the height of forests, the density of vegetation, and providing detail on the Earths carbon cycle. In this blog post, we will explore the GEDI dataset by leveraging IPFS with the ipfs-stac python library.
2024-07-26 - Workshop Executive Summary Now Live!
We’re excited to share the executive summary of our Workshop on Open Geospatial Science and the Decentralized Geospatial Web! This one-and-a-half-day event brought together over 30 experts from innovative organizations, federal agencies, and universities to explore how open science and decentralized web technologies can enhance the geospatial sector. This summary captures the key discussions and insights from the workshop, highlighting the potential to boost data accessibility, transparency, and collaboration. The full summary can be found here
2024-04-18 - Workshop Recap
The EASIER Data Initiative would like to extend our appreciation to everyone who participated in and was involved with our Workshop on Open Geospatial Science and the Decentralized Geospatial Web! This was a one-and-a-half-day event with 30+ domain experts and leaders from innovative organizations, federal agencies, and universities who cultivated discussion and showcased projects covering the overlap between geospatial science, the decentralized web, and open science.
2024-01-25 - Using Content Addressable aRchives to store decentralized data
When dealing with large geospatial datasets, such as Landsat 9 imagery, effective management, storage, and seamless transfer of massive data are key challenges for both data stewards and users seeking to access the content. One common solution for bundling and compressing these datasets is using
.tarfiles (a type of File archiver), which aids data stewards in organizing into collections and simplifying data transfer for users. Transitioning into the realm of decentralized systems, Content Addressable Archives (CAR) present a solution akin to.tarfiles. CAR files, like their traditional counterparts, play a role in maintaining data organization and accessibility in decentralized setups. In this blog post, we’ll look into the technical details of CAR files and explore some practical advantages inherent to InterPlanetary File System (IPFS) and Web3.2024-01-24 - Project Showcase - Web3 Geospatial Dashboard
What does user access to geospatial data look like in Web3? How can they find data or download content? Spoiler alert! Not much different than today but with the advantages of a decentralized network. One of the projects currently being fostered by The EASIER Data Initiative has been focused on user accessibility of decentralized content with a Web3 Geospatial Dashboard application and a Chrome extension. The dashboard is the entry point for a user to discover content, specifically Landsat 9 Collection 2 Level-1 imagery. With the Chrome extension enabled, a user is able to “pin” an image from our implementation of the SpatioTemporal Asset Catalogs (STAC) running alongside our IPFS node. Let’s dive into the details of our Web3 implementation.
2024-01-22 - Proof of concept for querying spatial data on IPFS using geohash
In this post we’ll be exploring how to store and access geospatial data in a decentralized way using Geohash and InterPlanetary File System (IPFS). Geohash is a spatial indexing system that divides the Earth into a grid of squares and assigns a unique identifier to each square. IPFS is a peer-to-peer network that uses Content Identifiers (CIDs) to store and share data. By joining geohash to CIDs of spatial features , we can create a hierarchical indexing system that organizes geospatial features into a directory system based on the geohash encoding. This allows us to store and query geospatial data on IPFS, without relying on centralized servers or databases.
2024-01-19 - Preloading a cache for a tiered storage system
In this blog post, we explore the practical aspects of tiered storage systems, focusing on the nuances of hot and cold storage layers using different cache system strategies. The premise starts with the system’s initialization and an empty hot layer or cache and illustrate the impact of prepopulating a cache with randomly selected geospatial data. Specifically centered around landsat scenes within the continental US, the two layers—cold storage and hot storage (set up as a least recently used cache)—play key roles in the experiment. We’ll delve into the mechanics of data requests categorized as region, state, or county, and how the system strategically manages landsat scenes in the least recently used cache.
2023-08-30 - NDVI on Landsat 9 imagery with data using IPFS
Geospatial professionals frequently handle vast data sets, like satellite imagery, for tasks ranging from object detection to land cover classification. The Normalized Difference Vegetation Index (NDVI) is a widely-used metric for assessing vegetation health in a given area. In this blog post, we’ll walk you through calculating NDVI using Landsat 9 imagery and the IPFS network with the help of the Python library, ipfs-stac, in a Jupyter notebook. We’ll also discuss the benefits of leveraging IPFS in geospatial workflows, such as content-addressing and decentralization.