It’s been a great year for open data and a beautiful year for satellite imagery. Just for fun, here’s a few favorites.

Confluence of the Fisher and Mellor glaciers, Antarctica. On November 11th, as the Southern Hemisphere’s summer began, Landsat 8 passed over the area of East Antarctica where the Fisher and Mellor glaciers join, forming part of the huge Lambert Glacier System — the largest in the world, although it was only mapped in the 1950s. The blue streaks are bare ice, where the wind has blown away all loose snow. Astronomers search places like those to find meteorites, lifted to the surface where the ice is forced up slopes.

Ships in Singapore Harbor. Singapore is one of the busiest container ports in the world, and there are so many cargo ships anchored around it that you can follow the currents by seeing which way they’re pointing. Many of the ships here are larger than the Titanic, and can carry as much cargo as the weight of a million people, or three hundred air-freight 747s loaded to capacity. Landsat 8 — undaunted by thick tropical haze — picked this up on June 27th.

Haiyan. On November 8th, Supertyphoon Haiyan (or Yolanda) made landfall, with recordbreaking wind speeds usually only seen in tornados. Open satellite imagery was a vital part of storm prediction as well as recovery. This MODIS image has the outline of the Philippines superimposed, since the hurricane completely covers the nation.

Fog around Santiago. Chile’s capital city is sandwiched between the huge Andes mountains to the east and lower coastal ranges to the west. In this Landsat 8 view from August 2nd, fog has crept into the valleys of the western mountains, but Santiago and the surrounding wine country is mostly clear.

The Rim Fire. In August, September, and October, one of the largest and most spectacular wildfires in California’s history burned near Yosemite. This Landsat 8 scene from August 24th shows the edge of the fire as it moved south. Like all Landsat imagery, it was available for free to the public — and firefighting agencies — within hours, allowing same-day planning.

Pavlof eruption. Sometimes the most interesting satellite images are by people. On May 18th, astronauts on the International Space Station used ordinary digital cameras to record a volcanic eruption in the Aleutian Islands, Alaska.

Rice farms north of Shanghai. The delta of China’s Yellow River has created extremely fertile farmland. In this Landsat 8 scene from August 13th, we can see the dense rice paddies of Rudong County, only two hours’ drive north of the skyscrapers of downtown Shanghai. The patterns to the east are sand islands where the river meets the Pacific.

Everything you see here is free to download and use at original size, thanks to the efforts of NASA, USGS, NOAA, and other agencies — just follow the links.

Did I miss your favorite image? Tell me on Twitter!

Geologist Ryan Clark joins the team! Ryan got into coding while maintaining the data behind various geologic maps at the Arizona Geological Survey. His deep technical skills range from maintaining server infrastructure to building Node.js-powered applications and designing user-interfaces for complex data entry. Recently Ryan’s been aggregating geothermal data from across the United States into a national data network. Passionate about providing scientists with simple, open-source tools that help keep their data flexible, open, and accessible — there is no doubt that Ryan is going to fit in with the team culture.

While Ryan will be working across a number of projects with our development team, we’re excited to see him hit the ground running by working on TileMill 2. He’ll also be digging deeper into OpenStreetMap to help keep our world wide base map, Mapbox Streets, the best map in the world.

Civic hacker Mick Thompson joins Mapbox in San Francisco! We first started talking maps with Mick at Code for America, where he was a 2012 Fellow and most recently the Engineer in Residence. His years making government software simpler and easier to use will help us build even better APIs that power Mapbox.com and create amazing user interfaces as we expand our data editing tools. Welcome Mick!

Paul Goodman is joining our expanding team in San Francisco, helping us grow our government and agriculture tools in addition to contributing to broader business strategy across Mapbox. His background in international development is going to be a great win and prior to joining Mapbox Paul started Acopio, a technology platform that helped coffee farmers track yields and better manage their supply chain. Keep an eye out for Paul in SF and beyond.

Design consultant Eleanor Lutz joins the Mapbox team! As a designer and molecular biology major, Eleanor is experienced in bringing her design skills to anything from custom-made research equipment to intricate brain surgeries on mosquitoes. She’ll be jumping into Mapbox design projects in map styling, illustration, and data visualization.

Android developers, we’ve heard you! Check out the new Mapbox Android SDK repository, read the quick start guide, and start sending your feedback for where we should go next.

Based on OSMDroid, a popular Java library for embedding OpenStreetMap in Android apps, our SDK brings support for the latest versions of Android, easy integration with Mapbox map IDs, and lays the groundwork for the features we’ll be adding and improving upon.

To quickly proof our work, we’re building a demo app as we develop the SDK. You can try the latest version by downloading the APK right to your device.

Like the rest of Mapbox’s developer tools, the Android SDK is open source. Any comments, suggestions, or bug reports are welcome: just post on the GitHub issue queue. We’re looking for Androiders to try the demo app on as many devices as possible, so ping me (@fdans) or Justin (@incanus77) on Twitter with your thoughts!

We just rolled out an update to our developers landing page. We built the new design to be more than just a jumping off point for developer resources: it’s also a guide for starting to develop with a map id right away.

Are you a developer that uses Mapbox? Check out /developers and let us know on Twitter what we can do to improve even more.

Update: launch has been pushed back due to solar weather!

Today this rocket will launch Planet Labs’ new imagery satellites to space:

They sent up a couple test satellites in the summer, but this will be a whole flock, as they call it — 28 satellites, more than doubling the number of commercial cameras in space. They can launch so many at once because unlike traditional satellites, which are the size of fridges, each of Planet Labs’ “doves” is the size of a shoebox:

Planet Labs Flock 1: they’re all going to space today.

These nimble little satellites represent a new generation of imaging hardware that’s all about getting more data faster. The whole industry is moving in interesting directions, and Planet Labs is out on the forefront. We wish all our friends there the best of luck with this launch, and expect great things for years to come.

NASA TV Wallops will carry the launch live, starting with interesting background material in the morning.

I made a woodcut-inspired, textured map to showcase the complexity of urban streets. The map was created in TileMill 2 using different wooden textures for everything from parking lots and hospitals to wetlands and forests.

A close-up look at Tokyo, Japan.

I also added custom wooden placards behind every country name and wooden stars to mark major cities around the world.

To make the placards for each country, I first made several copies of the image in different sizes. Then I sorted the country names by length in Java so that long names like “The Dominican Republic” were in a different category from shorter names like “Peru.”

Sorting the names made it easy to assign different placard images at each zoom level. When the map zooms out far enough those country names are abbreviated to two letters and the placards change size to better fit the shorter name length.

Boston, Massachusetts

Seattle, Washington

Come get some drinks with the Mapbox West team at 7pm this Tuesday, the 14th. We’ll be at our favorite bar by the San Francisco office: Bloodhound. A contingent of the Mapbox East team will also be in town opening our new office space in SOMA.

Mapbox.js is moving forward to fit our powerful new drawing tools on Mapbox: today’s version 1.6.0 introduces support for L.mapbox.featureLayer, a successor to L.mapbox.markerLayer that supports lines and polygons as well as simple markers.

With the introduction of styled line and polygon features we also extended the simplestyle specification, an open standard for styling GeoJSON data. This means that the features that you style in the Mapbox interface will display the same in Mapbox.js, and your own simplestyle-formatted data will be styled.

We’re making developer documentation work better: Mapbox.js is a Leaflet plugin, but in the past the documentation for the two projects was in different places and hard to cross-reference. Now, you can browse both Mapbox.js and Leaflet documentation in the same place, and you have the choice to view it as short pages or ona single, huge comprehensive page.

This year’s US-based OpenStreetMap conference will be in beautiful Washington, DC on April 12 and 13. Come out for the two day event packed with sessions on the future of OpenStreetMap, the free and editable map of the world. Like last year’s conference in San Francisco, State of the Map US will bring together a wide array of OpenStreetMap contributors and users, businesses, government, nonprofits, curious first timers, and long time enthusiasts. With event planning kicking into high gear, ticket sales, the call for proposals, and scholarship applications are now open.

Audience at State of the Map US in San Francisco 2013

Here is a quick run down on how to get involved:

• Convinced and ready to attend? Buy your ticket for just $75 before February 15.
• Still deciding but want to stay in the loop? Sign up for email updates and the State of the Map US team will keep you posted.
• Built something great with OpenStreetMap, or have an idea for the direction of the project? Submit a session proposal.
• Want to come but need a little support to make it happen? Apply for a scholarship.
• Curious about what State of the Map US is all about? Watch videos from last year’s San Francisco conference.

If you have any questions around the event, tweet at @sotmus or just drop the State of the Map US team a line.

Maps can be a challenge in low bandwidth scenarios—they rely on dynamic content being loaded fast and being constantly interactive. We’re tackling this challenge with native mobile SDKs and fast APIs: here are a few ways the full-stack performance of Mapbox is killer.

Server-side compositing

When you embed a Mapbox-hosted map into your site or app, you refer to it by including the map ID, e.g. justin.gocbkbg3. A tile image URL for this map might look like the following:

https://api.tiles.mapbox.com/v3/justin.gocbkbg3/13/1303/2930.png

Another map ID on my account is justin.pdx_overlay - a neighborhood boundaries layer with partial transparency that I designed in TileMill using open data from Portland’s RLIS portal. Let’s look at the same tile from that map:

https://api.tiles.mapbox.com/v3/justin.pdx_overlay/13/1303/2930.png

And a labels-only layer custom made like the terrain layer:

https://api.tiles.mapbox.com/v3/justin.gn9hkiae/13/1303/2930.png

Creating a map of these three combined would traditionally require a tile from each map at each position, making the performance equation simply: three times slower than a single map.

We changed that with on-the-fly compositing: you can combine map layers in the URL with a comma, and only one request is required.

https://api.tiles.mapbox.com/v3/justin.gocbkbg3,justin.pdx_overlay,justin.gn9hkiae/13/1303/2930.png

Note how the labels appear over the colored regions.

These composite tiles are served at scale from our global network after being generated in milliseconds behind the scenes with the open source node-blend library.

Image quality API

Need to squeeze more tiles for every kilobyte? The Mapbox image quality API dynamically resamples images, changes PNG bit depth or JPEG quality, and converts between the two formats on the fly.

Take the terrain base layer mentioned above:

https://api.tiles.mapbox.com/v3/justin.gocbkbg3/13/1303/2930.png

You can change the file extension to grab an 80% quality JPEG image of the same tile:

https://api.tiles.mapbox.com/v3/justin.gocbkbg3/13/1303/2930.jpg

But you can also append a PNG bit depth or JPEG quality number:

JPEG	PNG
...13/1303/2930.jpg (original, 19KB)	...13/1303/2930.png256 (42KB)
...13/1303/2930.jpg90 (28KB)	...13/1303/2930.png128 (31KB)
...13/1303/2930.jpg80 (19KB)	...13/1303/2930.png64 (24KB)
...13/1303/2930.jpg70 (15KB)	...13/1303/2930.png32 (19KB)

In many cases, the difference isn’t obvious visually, and in others, the trade-off of speed for detail makes sense. Your app or site could even do bandwidth detection and adjust the map quality on the fly for the best download experience. You can combine the image quality API with the compositing API, since each of these tiles is actually a server-side composition of three separate source tiles.

Built for mobile

Both the compositing and image quality APIs let you customize your maps for speed and low-bandwidth. Whether you use our REST API to load images directly or our iOS SDK with full support for the image quality API, Mapbox is mobile-ready and built with the best possible user experience in mind.

Mozilla just published a new map showing the growing coverage of Mozilla Location Services. Mozilla announced Location Services late last year as a pilot project to provide geolocation lookups based on cell tower and WiFi access point information. These geolocation lookups can be used where GPS is not available or not accurate enough. The map shows 27 million measurement locations gridded to 10x10 meter cells. Working with Mozilla’s team we created a dark variation of Mozilla’s custom branded Mapbox maps, and rendered out the high density data using datamaps down to zoom level 15.

Coverage in San Francisco

The map uses a dark variation of Mozilla’s custom branded maps

We’ve split labels from the rest of the map to show them on top of the data

Head over to Mozilla for the interactive version of the map.

A chemical spill in West Virginia has left thousands in the West Virginia capital of Charleston and surrounding counties without drinking water. The 4-Methylcyclohexane Methanol spill took place yesterday on the Elk River just upriver from a West Virginia American Water facility.

The Waterkeeper Alliance quickly illustrated affected areas with this map:

Mapbox.com allows you to combine custom basemaps with multiple data layers and hand-drawn annotations. From there, we use on-the-fly compositing to deliver highly custom, highly performant maps.

As Bruno described last month, we leverage the Landsat Ecosystem Disturbance Adaptive Processing System, or LEDAPS, software routines to process Landsat Level 1 images to surface reflectance. This conversion is one of the most important steps in our larger imagery processing pipeline.

Surface Reflectance

Surface reflectance measures the ratio of solar radiation reflected from, rather than absorbed by, the Earth’s surface. To achieve the most accurate reflectance measurement, the effects of the atmosphere need to be taken into account. Landsat surface reflectance products contain the measurements the sensor would capture under ideal conditions — held right above the Earth’s surface without any effects caused by the atmosphere.

Surface reflectance products are ideally suited for change detection and monitoring applications, as removing the atmospheric effects makes it easier to compare images over time. Many higher-level surface geospatial analyses — including vegetation indexes, albedo, Leaf Area Index (LAI), burned area, land cover, and land cover change — rely on surface reflectance products.

The difference is striking — as you can see in the before and after over Hong Kong below.

Left: Original L1 image. Right: Surface reflectance, after LEDAPS Atmospheric Correction.

Q&A

We recently got in touch with Calli Jenkerson, Senior Scientist at the U.S. Geological Survey (USGS), and John Dwyer, Project Scientist for the Landsat Data Continuity Mission (LDCM) ground system, for a Q&A about LEDAPS' development and applications. (Note: We’ve added links to some of the concepts and resources Calli and John refer to in their answers)

What is the USGS' relationship to LEDAPS?

Calli & John @USGS: The USGS has assumed stewardship of the LEDAPS code. LEDAPS was developed by scientists from NASA Goddard Space Flight Center (NASA GSFC) and the University of Maryland under a project funded by NASA MEaSUREs. Once the NASA funding expired, the USGS worked collaboratively with the original developers to fix known issues, introduce some enhancements, and assume responsibility for maintaining the code.

The USGS assumed this responsibility because it was aligned with our program objectives to develop climate data records (CDRs) and essential climate variables (ECVs) from the 40+ year Landsat record of observations.

What does the LEDAPS software offer to end users of Landsat products?

Calli & John @USGS: LEDAPS is used to retrieve surface reflectance values from Landsat TM and ETM+ data. This involves the use of auxiliary data (ozone, water vapor, geopotential height, aerosol optical thickness) to correct for molecular scattering and absorption by atmospheric constituents.

How does LEDAPS improve on what came before it?

Calli & John @USGS:The standard Landsat products (Level-1T) that are distributed today are radiometrically calibrated (scaled radiances), and orthorectified using ground control points for precision correction and digital elevation models to correct for relief displacement. LEDAPS processes the Level-1T data to surface reflectance, which enables quantifying land surface change after having normalized the data for sensor viewing and solar illumination geometry and atmospheric correction.

Who are the intended users of LEDAPS?

Calli & John @USGS: The immediate stakeholders for these products are scientists, although accurately processed surface reflectance retrievals are useful for data visualization, academic researchers and educators.

What are some challenges associated with using LEDAPS? What problems doesn’t it solve?

Calli & John @USGS: The greatest uncertainty associated with the LEDAPS products is with the aerosol correction. The algorithms use an empirical relationship between the SWIR and visible bands to estimate the concentration of aerosols as inputs to the 6S radiative transfer model. Dark dense vegetation targets within the given Landsat scene are used to estimate the aerosol optical thickness, therefore this correction becomes tenuous when there is insufficient dark dense vegetation within a scene, i.e. semi-arid and arid regions. Desert areas and snow and ice covered regions are not handled well by LEDAPS.

Not all remote sensing applications require the use of surface reflectance data. Alternatively, top of atmosphere (TOA) reflectance can be used to normalize scene radiances due to variations in solar illumination, sensor viewing geometry, and seasonality (Earth-Sun distance).

In what ways does LEDAPS pre-processing provide a superior product to Level 1 for Landsat imagery applications?

Calli & John @USGS: Typically, surface reflectance is required for more quantitative analysis and modeling of geophysical (albedo) and biophysical (LAI, FAPAR) parameters, or to minimize uncertainties in quantifying landscape change. Surface reflectance is not necessarily required for mapping land cover, but it may yield improved estimations of changes in vegetation condition (e.g. leaf moisture content, vegetation stress).

Are there any aspects of the software routines that could use further refinement?

Calli & John @USGS: We are working with other researchers to improve methods for detecting clouds and cloud shadows at the pixel level. Others have investigated the bidirectional reflectance distribution function (BRDF) effects when mosaicking data over large areas and through time. The adjacency effect of neighboring pixels on the bidirectional reflectance function of target pixels also needs further research and understanding.

Any idea when the LEDAPS software user community can expect Landsat 8 support?

Calli & John @USGS: We hope to have a capability for surface reflectance processing of Landsat 8 OLI data sometime in 2014, hopefully in the first half. NASA is the funding sponsor for that work.

At Mapbox, we leverage LEDAPS to process Landsat Level 1 images to surface reflectance as part of our larger processing pipeline to deliver more and more zoom levels of Cloudless Atlas to our users. We’re grateful to Calli Jenkerson and John Dwyer of the USGS for their time chatting with us, and for the USGS and NASA’s commitment to developing open source code and promoting accessible open data policies.

Eric and I are going to be hitting Boston this week and would love to have some beers with you. Brave the cold and join us this Thursday, 6:30pm at Cambridge Brewing Company to see what Mapbox has been up to behind the scenes — we’ve got some fun things to share.

Tableau users can now add custom base maps to any visualizations across their business intelligence and analytics software product. The Information Lab, who implements and tailors Tableau’s software for major customers worldwide, just launched these new base maps so Tableau users can make cleaner looking data visualizations. Here is a quick look at the maps that launched this morning, integrated into Tableau:

Mapbox.js can be much more powerful with the help of plugins: everything from clustering markers to loading OpenStreetMap data has been implemented by members of the community and is ready to connect. Today we’re making this simpler and easier withMapbox.js plugins as a fast, hosted API.

Now, instead of downloading and copying plugin distributions, you can just hotlink the source files just as you do with Mapbox.js. This makes it easy to mix and match features and build fast.

We’re starting with eight high-quality plugins:

• Leaflet Draw by Jacob Toye– Adds drawing functionality to your map.
• Leaflet Fullscreen – Enables the map to fill the screen using the HTML5 Fullscreen API.
• Leaflet Markercluster by Dave Leaver– Groups nearby markers into clusters to unclutter the map.
• Leaflet Hash by Michael Lawrence Evans– Adds information to the URL in your browser that makes it easy to link to specific map views.
• Leaflet Image – Export images of a map without a server component.
• Leaflet Label by Jacob Toye– Adds labels to markers and other vector layers that can be shown when hovering over that item or bound to other JavaScript events.
• Leaflet Locate by Dominik Moritz– Adds a button to find the user’s location using the HTML5 Geolocation API.
• Leaflet OSM by John Firebaugh – A convenient way of rendering tiles and vector data from OpenStreetMap.

We’ve added an example to the examples gallery to demonstrate how every plugin on the site can be used. Just copy example code or reference the scripts to start integrating these into your site.

Just like Mapbox.js, these plugins are based on and compatible with Leaflet— so you can use them with or without Mapbox.js. Stay tuned for more in the future.

Today we’re releasing Mapbox.js v1.6.1, which includes bug fixes, documentation improvements, and a system that will make it radically easier to use plugins to improve your map.

This release includes an updated version of Leaflet, v0.7.2, that fixes a shipped bug in Google Chrome 32.

Tristen pushed a total redesign of the Mapbox.js site that’s faster and easier to use: our wealth of examples is now easily browsable and searchable.

API Documentation is essential, and we’re working on making it simple, connected, and accurate. This revision refines the smart interlinking of articles so you can instantly jump from a page about Mapbox.js to its references in Leaflet.