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A Modern Imagery Processing Pipeline

Fri, 04/17/2015 - 15:00



Satellite data is a tremendously powerful resource for governments and development organizations. We built a suite of tools to make open Landat data more accessible and useable. These allow our development partners to process imagery and perform analysis quicker, and that can make all the difference in rapidly evolving situations.

Often our partners need commercial imagery with greater resolution and refresh times than what Landsat 8 offers. We have great partnerships with commercial imagery providers, to offer all sorts of imagery. Too often receiving and processing commercial imagery is a huge pain point that slows us down and makes it harder to make use of the data. As developers we know it could be better.

Astro Digital gave us an opportunity to rebuild this workflow from the ground up. We’ve worked closely with their team to build a satellite imagery pipeline for developers and end users. We just launched a browsing and publishing platform with Astro Digital to allow anyone to discover, process, and share satellite imagery in an incredibly quick and intuitive manner. A process that previously could take days has now been cut down to minutes.

API first

We built an end-to-end data processing pipeline that feeds a powerful data API that unlocks possibilities for others. We broke down the fundamental goals of the Platform and built API calls around each. Those goals were to search, process, and publish.

We built and exposed a perfomant Elasticsearch-powered endpoint, based on our previous landsat-api work, that will allow for complex queries to find exactly the data that is needed.

$ curl[0+TO+20]

But how to process the imagery? We extended our existing open source landsat-util tool to handle varying band combinations and the API offers several including true color, vegation health false color and urban false color.

$ curl

And finally, there is a simple request that can be made to process the imagery and receive a tiled map URL. This URL can be used with tools like Mapbox or Leaflet to build upon the processed imagery in any way. Full documentation, including interactive samples, can be found at

$ curl -X POST --data "sceneID=LC80430332014262LGN00&process=urbanFalse&satellite=l8" {"status":"Image is being processed."} Frictionless publishing

We are using this data pipeline to power an extremely easy and visual imagery browser and publishing tool. We started with Libra as a base and modified it to meet the Astro Digital specific workflow. Libra was already designed to be quick and intuitive. We added a simple publish workflow that will process and publish images and email a link to the tiled map after processing has completed. For images processed within this visual workflow, the email contains a link to an embeddable map that can be used anywhere across the web.

Working with Astro Digital, we built a modern publishing pipeline that we hope will push the entire industry to build more usable tools. This is good for the industry and good for users, particularly the small governments and development organizations that are the next wave of power satellite data users.

An OpenStreetMap for Government

Wed, 04/15/2015 - 14:00



The software that powers OpenStreetMap (OSM) is a fantastic open source tool for governments to manage road data. We are working with the World Bank and the Philippine government to deploy OSM software to create a collaborative tool for national and municipal authorities to manage road data.

OSM-as-a-platform gives governments a powerful open source option for enterprise management of road data. The OpenStreetMap community benefits with more investors in the OSM ecosystem and more OSM-ready data.

OSM and Government

OpenStreetMap is a powerful tool for collaborative mapping of critical infrastructure. Over two million OSM users have contributed 280 million road, rail, and waterway segments.

Governments are ideal OSM users. Governments build and maintain roads and manage public transit. Governments have powerful incentives to provide road data in a manner most useful to citizens. However legal, policy, and cultural considerations prevent governments from engaging in OSM.

  • Government may have a requirement for authoritative control of data for 911 purposes.
  • OSM’s share-alike requirements may run foul of government publishing obligations.
  • Agency IT Departments may be hesitant to authorize external services for maintaining critical infrastructure data.
  • Governments may have additional data or workflow requirements that aren’t supported by OSM.
OpenRoads - OSM for the Philippines

The National Government in the Philippines needs the ability to manage and collaboratively edit road data in much the same way as OSM, but with their own unique set of data and users. Different portions of the road network are managed by authorities at the national, provincial, municipal, or barangay. These authorities also need tools to manage data on road improvement projects, from evaluating proposed resurfacing projects to tracking the proper completion of a funded road expansion.

The OSM software provides fantastic tools for a whole-of-government approach to road management. Working closely with the World Bank, we are building the OpenRoads Network Editor, an OSM-based system for road management across Philippines’ various road authorities. We built this using OSM-as-a-platform and utilizing tools like iD and to-fix.

This tool will allow the Government to create a full map and inventory of the countries 200,000 KM of roads. Ultimately it will also provide the analytical tools to allow the Philippines to make better decisions on infrastructure investments with real-time data.

The World Bank recognizes the value of government managing data with collaborative open source tools. This week the World Bank is introducing this platform at a summit of the Philippine’s 1490 mayors. Today we are at the World Bank for Big Data for a More Resilient Future to present OpenRoads and how OSM-as-a-platform can benefit other governments.

Open Tools for Open Government

OpenRoads is part of Development Seed’s ongoing efforts to create open tools to support open government.

The OSM software ecosystem provides governments with a powerful open source alternative for managing road data. The OpenStreetMap community also benefits. These deployments create additional investors in the OSM software and more OSM-ready data.

Tracking Metadata in Real-time

Tue, 04/14/2015 - 14:00



This week we’re releasing more tools to track OpenStreetMap metadata. Together with the American Red Cross, we’re launching OSM Metadata API, a tool to help enable analysis of OSM’s rich metadata at the user and comment level. By using hashtags in changeset comments such as #MissingMaps, API access can enable groups like the Red Cross to gain feedback from OSM deployments.

We’re building off our osm-meta-util work we released several weeks ago to store and index OSM metadata. We built the API on Elasticsearch and Node.js. Using Elasticsearch, we index and store metadata information such as changeset_id, user, created_at, bounding box, comment. You can filter these logs by hashtag and keyword as they come in real-time, as well as build a database of historical metadata logs.

We’re rolling out a sample API today with the code. You can access the endpoint here:

You can use the full power of Apache Lucene Search to browse the data. For example, to browse for the #missingmaps hashtag between two dates:[2015-04-08T00:00:00Z+TO+2015-04-09T00:00:00Z]+AND+comment:"missingmaps"

In the past week, #missingmaps events have helped make 69,865 edits to OpenStreetMap in places like Haiti, Iraq, Zimbabwe, and Tanzania.

Check out the API guide for full endpoint documentation.

All code is available via a GitHub repository on OSM Lab. To get started quickly, you can deploy the API as a Heroku app. Try it out and contribute back to the OSM community.

Howdy, Anand Thakker!

Thu, 04/02/2015 - 16:00



Anand Thakker likes the messy stuff. Whether he’s using historic satellite imagery to measure electrification or finding better ways to teach high school math, Anand embraces hard problems. He brings creativity, energy, and thoughtfulness to every project.

Anand is going to help us to build powerful tools that make a real difference. Anand has always been engineering. He built a search engine with his high school friends. At a startup right out of college, he worked on tools for analyzing and debugging XML-based web services. Then, for seven years, he devoted his energy to teaching high school math and computer science in Baltimore. There too, Anand engineered curriculum and teaching methods for improving students’ problem solving skills.

Anand studied math and computer science at Carnegie Mellon University and has a masters in education from Harvard University. Give him a shout on github or twitter.

Welcome Emily

Thu, 04/02/2015 - 14:00



International development is hard. Emily first saw this at a school in Kenya for Somali refugees. She was 16. In the more than a decade since, Emily has pushed to improve peoples’ lives in places from El Salvador to Afghanistan. She’s been teargassed in Djibouti and battled bureaucracy back in DC.

Emily has worked on all sides of international development and has seen its successes and its failures. Emily is going to work on our business strategy and operations. She is going to help us to better solve hard social problems and to make sure our work is impactful.

We had the opportunity to work with Emily when she was at Democracy Internatinal to open up election data in Lebanon and Tunisia. She understood then the opportunities and the limits of data and technology to make meaningful change. We look forward in working with her to expand those limits.

Say Bienvenue or Hola to Emily on (twitter)[].

Optimizing Landsat-util

Sat, 03/28/2015 - 02:00



Two weeks ago we launched a new version of Landsat-util, v0.5, our utility for searching and processing Landsat satellite imagery. This version is now faster than it was before. To do this we rewrote most of the internals to use flexible python frameworks.

Below is a deep dive into how we’ve improved Landsat-util to make it faster and easier to use.

Dependency Hell

Landsat-util downloads Landsat files, pulls out the individual bands representing wavelengths of light, corrects contrast, warps them and combines them to make a colored image you can add on a web map.

Initially, landsat-util was written as a command line wrapper to existing pipelines:

  1. Scale bands with gdal-translate
  2. Warp with gdal-warp to the correct projection
  3. Combine bands with ImageMagick
  4. Contrast correct with OpenCV
  5. Pansharpen with orfeoToolbox
  6. Gamma correct with ImageMagick
  7. Add geographic information back with gdal_edit

We needed a lot of dependencies to process an image, and they’re not particularly optimized for scripting. ImageMagick, GDAL, orfeoToolbox and openCV are monolithic frameworks that don’t allow for cherry-picking functions. Installing all these frameworks can be a painful experience.

This toolchain combination is also tedious because it creates inherent bottlenecks. To communicate between all tools, we need to write temporary files to disk and read them back in at every step.

Enter rasterio

Rasterio is a great python library written by Sean Gillies at Mapbox to work with raster data. It wraps around gdal and abstracts the band data as Numpy arrays.

By standardizing the input, rasterio allows us to minimize our dependencies and use fast, in-memory, scientific libraries like scikit-image.

Here’s the guts of our new process:

Read in bands with rasterio with rasterio.drivers(): with'LC82040522013123LGN01_B4.TIF') as band4: with'LC82040522013123LGN01_B3.TIF') as band3: with'LC82040522013123LGN01_B2.TIF') as band2: with'LC82040522013123LGN01_B8.TIF') as band8: band4_s = band4.read_band(1) band3_s = band3.read_band(1) band2_s = band2.read_band(1) band8_s = band8.read_band(1) Scale bands with scikit from skimage import transform as sktransform band4_s = sktransform.rescale(band4_s, 2) band3_s = sktransform.rescale(band3_s, 2) band2_s = sktransform.rescale(band2_s, 2) Warp with rasterio for color, band in zip([r,g,b,b8], [band4_s, band3_s, band2_s, band8_s]): reproject(band, color, src_transform = src.transform, src_crs =, dst_transform = dst_transform, dst_crs = dst_crs, resampling = RESAMPLING.nearest) Pansharpen using numpy operations m = r + b + g pan = 1/m * b8 r = r * pan b = b * pan g = g * pan Contrast-correct and gamma correct using scikit-image # using CLAHE from skimage.exposure import equalize_adapthist for band in [r,g,b]: band = equalize_adapthist(band, clip_limit=0.02) Write to disk using rasterio with tiffname,'w', driver='GTiff', width=dst_shape[1],height=dst_shape[0], count=3,dtype=numpy.uint8, nodata=0, transform=dst_transform, photometric='RGB', crs=dst_crs) as dst: for k, arr in [(1, r), (2, g), (3, b)]: dst.write_band(k, arr)

The toolchain consists of only python libraries, and no other dependencies. Rasterio inherently supports GeoTiff so we don’t lose geo-information along the way.

Landsat-util is open source, and we encourage developers to improve on our process. Fork our repo!

How fast?

By using rasterio, numpy and scikit, we eliminate the disk bottleneck, and we regain transparent control over the pipeline.

We ran tests on a couple of AWS machines. Each test was conducted 5 times and the resulting times were averaged.

  • R3.large: 2-core 2.5 GHz Intel Xeon (Ivy Bridge) with 15GB RAM and 32GB SSD.
  • C4.2xlarge: 8-core 2.9GHz Intel Xeon (Haswell) with 15GB RAM and 32GB SSD.
Results instance type process type old-landsat-util new-landsat-util speedup R3.large non-pansharpened 252.7s 122.05s 2x R3.large pansharpened 846.9s 349.17s 2.4x C4.2xlarge non-pansharpened 216.6s 106.67s 2x C4.2xlarge pansharpened 438s 290s 1.5x

On all but the largest machine, the new landsat-util is at least twice as fast as previously.

And it gets better: a significant amount of time (about a minute on average) is used up to decompress the NASA bundle after downloading it. Landsat-util v0.5 takes advantage of AWS’s new landsat archive of unzipped bands, saving even more time.

We hope you enjoy the new landsat-util! Fork it, modify it, break it or just use it and tell us about all the ways you’re incorporating satellite data in your apps.

We're in the Philippines

Fri, 03/27/2015 - 10:00



This week, Ian and I are in the Philippines working alongside the World Bank to set up collaborative, open source tools for government to track critical infrastructure, particularly road data.

Good road data is crucial for smart development and disaster response. To help the national and local governments manage their road infrastructure, we’ll be building on open tools such as iD, the popular editor for OpenStreetMap. Our goal is to use open-source tools to create better data infrastructure, at an enterprise level, tailored to the information needs of government.

We are confessed fans of OpenStreetMap and the ecosystem that surround it. These tools are battle-tested and optimized for the quantity of data that flows daily through OSM. We think these tools can make government work better, and so far we’ve seen terrific responses from officials here.

We’ll be in the Philippines until next Tuesday. Ping us on Twitter if you’d like to meet and talk OSM and managing infrastructure data.

Bem-vinda Caroline Portugal

Tue, 03/24/2015 - 10:00



Welcome Caroline! Designer and architect Caroline Portugal is joining Development Seed.

Caroline is going to build thoughtful, beautiful software. Caroline hails from Brazil, where she studied and practiced Architecture and Urban Planning. She worked on architecture projects in Brazil and the US ranging from Rio’s Olympic Park Master Plan to the National Museum of African American History & Culture. Caroline’s latest work has been in visual design. She recently completed a graduate degree in Graphic Design from MICA.

Caroline’s work is moving. It is visually stunning. It is grounded in solving practical challenges with humanity, creativity and fun. Check out her work on bike lanes and the future of contraception to get a sense of why we are delighted to have her on the team.

Caroline speaks fluent Portuguese. Say bem-vinda to Caroline on twitter.

Powering Landsat Powertools

Thu, 03/19/2015 - 14:00



Amazon Web Services just opened Landsat on AWS, a publicly available archive of Landsat 8 imagery hosted on their reliable and accessible infrastructure. This investment by the AWS open data team has a big impact on our work to make satellite imagery more accessible.

Our open source Landsat browser, Libra, now has options on some scenes to download individual bands related to specific types of imagery analysis like NDVI, or Urban False Color. The most recent Landsat–8 images are now available for download up to two days sooner. Last week we rolled out a new version of landsat-util, our open source utility for processing Landsat imagery. The new version is much faster and allows you to build false color composites on the fly. These improvements to Libra and landsat-util are possible because we started using Landsat on AWS, which is a publicly available archive of Landsat 8 imagery hosted on Amazon S3 that is publicly available today.

Our newest releases of Libra and landsat-util utilize Landsat on AWS for 2015 imagery. Landsat on AWS provides 2015 imagery as unzipped individual bands. AWS makes this imagery available extremely quickly, often within hours of capture. We can pull only the data that we need and to work with it immediately.

Landsat 8 imagery is an incredibly powerful resource. It is some of the most valuable open data produced by the US Government. Our partners rely on Landsat data for everything from evaluating droughts to tracking conflict. However, until now, individual bands of Landsat imagery has never been available via predictable download endpoints that we can integrate into our tools.

Libra and landsat-util now allow our partners to get imagery sooner and process it faster. Speed and ease are critical to our partners who use this data to respond to natural disasters, prevent hunger, and monitor elections.

Thanks to the AWS team and collaborators–Frank Warmerdam at Planet Labs, Charlie Loyd at Mapbox, and Peter Becker and others from ESRI–for building a Landsat archive with developers in mind.

Libra and landsat-util are open source and on Github. Go ahead and fork or contribute.

Launching v0.5 of landsat-util

Tue, 03/10/2015 - 15:30



We just released a new version of landsat-util, version 0.5. This version is lighter and has fewer dependencies. Landsat-util v0.5 downloads and processes images much faster and gives users more control.

Installing landsat-util now is considerably easier for Mac and Linux users. We are still working to make it as easy to run on Windows.

This new version reflects a significant change in our thinking on landsat-util. We've moved to using faster and simpler frameworks to optimize processing. We removed heavy dependencies, such as ImageMagick and Orfeo Toolbox, that were causing installation problems. We leveraged faster processing frameworks like Rasterio, numpy, and scipy. These changes significantly optimize disk and memory usage resulting in faster and less error-prone processing. Landsat-util can now process images 3x faster.

We'll post more of the technical details on how we rebuilt landsat-util and what's under the hood. In the meantime hit us up at FOSS4G in San Francisco all week to learn more.

Collect and verify mobile reports

Tue, 03/10/2015 - 14:30



A common scenario for mobile reporting looks like this:

  • A group wants to collect reports from their own trusted network and also to crowdsource reports from the public.
  • The group has some process to try to verify crowdsourced reports and needs to track verification.
  • The group wants to publish this data using simple visuals that answer their core question and invites comparison.

We recently worked with a partner looking to do exactly this. With so many tools for mobile data collection, you'd think that this should be easy to do with open source tools. It's not. Here is our experience.

Collecting Data

OpenDataKit is a great open source tool for data collection on Android. When you have control over the device your volunteers are using, ODK is great. (Side note: If you like ODK, check out OpenMapKit an exciting project of the American Red Cross, SpatialDev, and Ona).

When you are relying on reports from volunteers with their own phones you can't always get them to download an Android app. This is why the ODK ecosystem and tools like Enketo and Formhub are interesting. Enketo allows us to serve a simple web form on any device. Formhub gives us the ability to convert and manage Excel-based forms. While this ecosystem is great for authoring forms, we found that this system had some shortcomings:

  • managing multiple datasets can be a challenge,
  • API lacks some features,
  • system is resource hungry, and
  • difficult to deploy and maintain.

We used these tools for what they are best at, generating web forms from Excel documents. We pulled the data via the API to use in other open source tools.


No tool that we looked at had good workflow for verification. Most had no verification or had only a simple yes/no toggle. To make the verification of thousands of reports manageable, the system must have a workflow for quickly reviewing whether reports are verified true, verified false or are unverified.

We used Django to build a verification platform that is useful and usable for the data verifier who is triaging hundreds of reports a day and for the field organizer who is trying to track the status of single report.


To publish and visualize the collected data, we designed a map and report interface. This interface includes a map view, list view, and charts that invite comparison between incident report types and vicinity.

It was important to clearly show users whether information is verified. For simplicity, we use a simple checkbox to filter out all reports that are "unverified" or "verified false", even though we distinguish between these in the verification platform. We showed the verification status prominently on all report listings.

Reporters can select a neighborhood and choose a level of geographic precision. To protect the privacy of reporters, the system does not require reports to submit precise location information. Trusted administrators use the precise location when it is provided to verify reports. Regardless of the location precision, the public platform only shows reports to the neighborhood level.

Join our talk on Wednesday morning to hear more. We'll follow up later in the day with a Birds of a Feather session discussing how the open source data collection community continues to grow.

Hello San Francisco; Ready for FOSS4G

Mon, 03/09/2015 - 18:00



Landsat 8 image over San Francisco on December 31, 2014

We're out in San Francisco this week for FOSS4G North America. Look for Alireza and I at the conference or at our session Wednesday morning at 10:30am. Alireza will discuss using the OpenDataKit ecosystem to build mobile reporting and verification tools for refugee camps in Lebanon. We'll also be at the Birds of a Feather session discussing the future of OpenDataKit.

This week we will release new versions of landsat-util and Libra with some powerful new features. We are also working on some tools for managing OpenStreetMap data. We look forward to collaborating with old and new friends in the open source community to improve the tools for mobile data collection, Open Geo, and satellite imagery processing. Tweet us at @nas_smith or @scisco7 to meet up.

Tapping into OpenStreetMap Metadata

Thu, 02/19/2015 - 18:00



We just launched v0.1 of a new tool to tap into OpenStreetMap changeset metadata. We built the tool in partnership with the American Red Cross as part of the infrastructure for tracking efforts such as #MissingMaps.

OpenStreetMap changesets give us access to a wealth of metadata information that is not specifically geographic but incredibly rich. Metadata is helpful in understanding the changing nature of OSM. This is different from using geographic APIs like Overpass because metadata contains commit text, number of edits, which editor was used, etc. With metadata, we can track hashtags, analyze commit text or aggregate user metrics.

In 2014 alone, users committed over 6 million changesets to OSM. As OpenStreetMap's metadata grows, dealing with the sheer amount can be daunting. We built osm-meta-util as an experiment in making OSM metadata easier and faster to use.

osm-meta-util focuses on two core functions: downloading the minutely compressed metadata files and serializing into JSON. We convert compressed OSM XML files containing multiple commits to a stream of JSON objects that can be piped to any tool or API.

You can use the library in a Node application or as a command-line utility to download all the data between two dates:

MetaUtil({ 'start': process.argv[2], 'end': process.argv[3], 'delay': process.argv[4] }).pipe(process.stdout)

In combination with jq, to get a commit history we can simply run:

node app 001181708 001181721 1000 | jq -c '{date: .created_at, text: .comment}'

If you don't give the tool any parameters, it will get the latest changesets and update every minute.

We're using this utility to experiment building a metadata API with the American Red Cross. But we know there are many more uses of the rich OSM metadata and want to see what others can do with the tool. Together with the American Red Cross we've put this on OSM Lab, a Github organization for OSM related projects. Follow the development of osm-meta-util on Github.

Hello Mariano Arrien-Gomez!

Thu, 02/19/2015 - 11:00



Mariano Arrien-Gomez is joining Development Seed. Mariano is going to help us by creating beautiful illustrations and impactful visualizations. Mariano takes his inspiration from the world around him and expresses himself via illustration, photography and murals. Check out his Instagram feed for some examples.

Mariano is an artist who brings a meticulous and thoughtful approach to our design workflow. We're excited to see his work begin to influence our projects.

Say hello to Mariano on Twitter or Github.

Open Data Day Garage Party

Fri, 02/13/2015 - 10:00



We love open data. And we love to talk about it over drinks with other open data lovers. Next Friday, February 20th at 7:00pm we are hosting an Open Data Day celebration in the Mapbox Garage. Head over to the Garage after the first day of Open Data Day DC to talk about open tools for open data.

Even if you can't make it to Open Data Day DC (or didn't grab a spot on the now closed list), come over and share the #opendatalove.

Let us know if you're in by RSVPing now.

Data Hungry Happy Hour

Fri, 02/06/2015 - 10:00



We'll be at the Thought For Food Global Summit next week working with some brilliant people on the biggest challenges in agriculture and feeding the hungry. Look for Olaf at the Summit.

If you are in Lisbon for the Summit, please join us for a Data Hungry happy hour on Thursday evening. We'll be geeking out on better data, satellites, and sensors can contribute to better food policy. The Happy Hour kicks off at 18.30 in Fabulas in the center of Lisbon. The first couple of rounds are on us. You can RSVP here.

Welcome Mariano Arrien-Gomez

Mon, 02/02/2015 - 10:00



Mariano Arrien-Gomez is an artist and designer. He builds beautiful graphics and data visualizations that make our products more compelling, creative, and humane. Mariano directs his design skills toward the issues and topics he is passionate about, from preserving local parks, to genetically modified food, to soccer. He utilizes a range of visual mediums including illustration, painting murals, and photography.

In addition to his work at Development Seed, Mariano is active in the DC art scene. Mariano received a Bachelors of Fine Arts in Graphic Design from Virginia Commonwealth University. He is fluent in Spanish.

Announcing Libra - the Landsat imagery browser you will love

Thu, 01/22/2015 - 18:00



We've been working with Dauria Geo to produce the most usable imagery browser. Today we are releasing Libra, a fork of the Dauria Geo browser for open Landsat data. Libra allows you to browse, sort, and download more than 275 Terabytes of open Landsat imagery as easily as booking an Uber.

Liberating Landsat

We love open imagery. The global development organizations and developing governments that we work with use open satellite imagery for everything from evaluating disaster response, to tracking deforestation, to planning for drought. For our partners, open imagery isn't just a matter of cost; it is a matter of licensing and distribution. They get immediate access to Landsat images and can analyze, manipulate, and distribute with almost no restrictions.

To make Landsat data more useful, we've made it easier to use. We built two open source tools for working with Landsat data - Landsat-util and Landsat API. It used to take all day for Development Seed's imagery specialists to turn Landsat data into imagery layers for online maps. With these two tools, any developer can do it in a matter of minutes.

These tools gave us a huge head start in building Libra. Libra relies heavily on Landsat API to quickly query by date, geography, and cloud cover and get image URLs, scene centroids, scene boundaries, and other metadata. Using Landsat API as a backbone of Libra also encouraged us to make improvements and configuration changes on Landsat API such as changing the limits on requests and returned data and some error handling.

Have a look at Libra and hit us with your feedback @developmentseed.

Introducing Development Seed Lisbon

Tue, 01/20/2015 - 10:00



Last week we announced the opening of Development Seed Lisbon. To kickstart our operations in Europe, we brought on our friends from Flipside, an experienced team working on meaningful open data projects for organizations around Europe. This move allows us to connect with partners and talent in the region, and also deliver quality work right out of the gate.

Olaf Veerman

Olaf will lead the Lisbon office, run projects, and help us establish a strong presence in Europe. He lived for many years in Latin America, working with small business networks, cooperatives and small farmer groups in Brazil, Uruguay and Venezuela. His experience in working with civil society organizatons around the world allows him to quickly understand our partners' needs and help them use technology to increase their social and economic impact.

You can connect with Olaf through Twitter.

Daniel da Silva

Daniel brings solid engineering skills that he applies to anything from building light-weight frontends with well structured APIs, to deploying tools for offline/online data collection. He is a quick learner and problem solver whose technology expertise spans PHP, Node, Angular, Jekyll and Mongodb. Daniel is going to help us pick the right tool for the job and deliver quality work to our partners.

Find Daniel on Github.

Ricardo Mestre

Ricardo is a talented designer and front-end developer who pushes how modern technologies can be used to craft usable and engaging websites. He worked for some of the biggest companies in Portugal, but is most passionate about free culture and equality, which he contributes to through his music, art and other projects. Ricardo is going have a big impact on the design and usability of our work.

You can find Ricardo on Twitter.

Development Seed opens office in Lisbon

Thu, 01/15/2015 - 11:00



Development Seed is opening an office in Lisbon. Our team grows by a continent today in beautiful Portugal where we will continue to build data tools and solve complex development challenges. Establishing an office in Europe puts us closer to our partners in Europe, Middle East, and Africa. It will also allow us to better connect to the talented open data hacker movement in the region.

To bootstrap our European team we are immediately bringing on all our talented friends at Flipside. The Flipside team have been doing fantastic work on projects ranging from opening data on clean energy, building mobile monitoring tools with Text To Change, and tracking forest fires around Portugal. The entire team joins Development Seed today. Olaf Veerman from Flipside will lead the Portugal office and help us to grow the team.

If you are in Lisbon, come and celebrate with us tonight at our Open Data Happy Hour.