The Open Data Cube and working towards a Digital Earth
Gavin, David
David's talk was the second talk in the "Remote Sensing" session at FOSS4G SotM Oceania 2019, organised by OSGeo Oceania and held at The National Library in Wellington, New Zealand from November 12-15 2019. FOSS4G SotM Oceania is the coming together of Oceania's geospatial open source and open data community - with four days of workshops, presentations, a community sprint and social events.
 Today, I'm here to talk to you about the Open Data Cube and working towards digital earth. As Kurt laid out in his presentation, there are two gold standards in open satellite data. Those are the Landsat missions, Landsats 5, 7 and 8, and the Sentinel 2 or the Sentinel missions. But today, I'll be focusing on optical satellite data. But rather than exploring the different bands that are available on each of the different platforms, I'll just focus in on the six bands that were common to all five satellites when we talk about Sentinel 2a and 2b. So from 1986 onwards, we have 30-meter resolution capture from Landsat with a 16-day revisit. So every pixel of the planet gets imaged every 16 days, and which means we have a tremendous archive of optical data that is incredibly sparse. From 2017 onwards, the second Sentinel 2 satellite was launched, and we now have 10-meter resolution and a five-day revisit. Combined, this is a fantastic way of being able to see our dynamic earth in action. So this is an image of a flood from in northwest Queensland in February of this year. This is the same area in October, so that's completely drought-ridden, no water. As Kurt showed, this is some of the bushfires burning in New South Wales right now, and with these six bands, we can see through the smoke, and we can see the firefronts burning in the sky. With these six bands, we can see unparalleled beauty of our planet, and we can do things like classify water. So that's what we can do with static imagery. If you just go out and download a single image or see it through a web service, that's the sort of thing that you can do. But if you want to do more, if you want to get your hands dirty with this data and derive new insights with it, then there's a tremendous amount of work that goes into actually acquiring this data, author-rectifying it, calibrating it, and then making it ready for time series analysis. So it's not just a matter of one photo to another. It's that every cell is ready to be interpreted and interrogated. So a couple of years ago, Geoscience Australia worked with the NCI and CSIRO to build the Australian Geoscience DataCube. In addition to actually processing all of this data and co-locating this data on the NCI, we set about creating an intuitive Python API, which is now known as the Open DataCube. In a nutshell, the Open DataCube provides a Python X-array interface to this sparse and rich data. So given a user-defined query, we go back to a Postgres database. We find all of the data sets that match your area of interest or any other attributes that you're querying on. Then the Open DataCube will group and stream the data back to you. So depending on whether you're just browsing or you're trying to do a whole set of processing on this data, this is all X-array, NumPy, and DASC enabled. So it's meant to be as intuitive and as a tool for rapid development and rapid insight gathering. So this is a tool our remote sensing scientists use. So that was a big paradigm shift in terms of empowering our scientific staff who already had knowledge of Python but gave them the knowledge to go further as well as to develop tools in concert with them. So with these tools, we can create Cloud-free mosaics. We can classify the ground cover using the joint remote sensing research program's fractional cover and in this ternary image, we have the bright greens as healthy photosynthetic vegetation, the blue as nonphotosynthetic vegetation, and red as bare soil. And in this, this is a representation of how often water is identified in a particular place. We have the deep blue or purple, the top is where, well, it's ocean, so we always see water there. And then with these reds and yellows, there are things that have been imaged maybe 5% or less of the time, so they're actually floods. So this particular color scheme helps you see both where standing water is and always has been but also where irregular inundations of water can occur. And recently we did a lot of work into mapping out the intertidal regions around Australia using Landsat. So using a tidal model, you were able to identify all of the low tide images and contrast them with the high tide images and develop a digital elevation model for this region of Australia. So at the close of the Australian Geoscience data cube, or rather the transition to digital earth Australia, we had all of this, we had petabytes of data located on the ANU supercomputer, which was fantastic for us to use and other users of the ANU, but it wasn't particularly accessible to our government partners and it wasn't particularly accessible to the public at large. So there were two driving goals of digital earth Australia. So that was to apply remote sensing science and satellite information to aid government decisions and policy making and to work with industry innovators to stimulate economic growth for Australia using satellite imagery. For us, the strategy was the same. We needed to make the data accessible, we needed to make the software more usable and we needed to work closely and alongside our stakeholders. So today, all of the data that DEA produces is available from NCI through threads or through Amazon web services via S3. We are looking at projects at the moment to make this data available through Google Earth Engine and Google Cloud Platform. We have worked and invested heavily with the Terrier and Data61 team to enhance the Terrier portal and to build a WMS interface on top of the open data cube. So in this, we're seeing a early prototype of some of the graphing and time series analysis that we intend to make more usable and more accessible to Kurt's dad. Well, we'll keep working on it so that people can combine things like stream gauge data or in this case, standing body, standing water body information alongside the imagery from and to be able to visualize what's going on. We have worked really hard with FrontierSI to make the Jupyter notebooks that our scientific staff have developed in available to everyone. So not only is the open data cube open source and are these notebooks available for people to download. This sandbox is available today. So if anybody took part in the open data cube master class yesterday, you would have had an opportunity to run on AWS hosted infrastructure running in Kubernetes, which you can go into more detail later. That basically allows users to go in and go out the data with Python and X array and scipy and do whatever they want. So last year, we participated with Murray Darling Basin Authority's Northern connectivity event. So this was a controlled release of water through the Murray Darling Basin to stimulate the river itself. It wasn't actually meant for farmers. It was because this is parts of the river had completely dried out and that's vital for the ecosystem that this all gets connected. There was suspicions. There was anticipation that not all of the water was necessarily going to make it all the way through. And a year later, we're in the news with satellite images question how private dams filled during Murray Darling pumping in Vargo. And actually, there's more controversial headlines that I could have put up, but probably get in trouble. So that's just one example of how we are working with other government agencies to improve policymaking for Australians. For the industry engagement, we also partnered with Frontier Si to work closely with private all sectors of private industry within Australia to figure out what we could be doing, how we could make this data more accessible, how we can make these insights more accessible, what do people want. So that was done through all of those metrics, which I won't read out. And it culminated in the ambitious Digital Earth Australia Labs program. So Labs is a grants program, grants, not grants. And Sandbox is a Jupiter hub environment. People do get these things mixed up. But so we have had three successful lab entrants that were working through at the moment. One of which is Sebo Labs. So their particular Labs project is to investigate using AWS serverless to enhance their pasture biomass products. So this is an information system that allows farmers to figure out how healthy their pastures and fields are so that they can do better rotation of their livestock and feed and be more responsible and successful. And to the culmination of a lot of this work has been, particularly this year, has been to create this capability for Africa. So we have, I wouldn't say partnered, we were very lucky that the United States Geological Survey Organization gave us access to their collection to data early. And we were able to, only for Landsat 8 at the moment, but it is anticipated that Landsat 5, 7, and 8 will all be available for the entire world at an analysis-ready processing level from, I'll say June next year. It's supposed to be earlier. That's the conservative estimate. But last week at the Geo-Ministerial event, we were able to show a cloud-free mosaic of Africa, which was just one of the many different derived and decision-ready products that we were able to produce and demonstrate. So this is all still a work in progress. This has its own sandbox if people want to go and play with this data. Once again, this is available through Terrier. And we are frequently being approached by other countries who want a data cube, want to find out more about how they can build a data cube. So if any of this is of any interest to you, so whether it's at the Australian data of DEA and DEA Labs, or whether it's Open Data Cube or Digital Earth Africa, or if it's how we used DASC to produce a cloud-free composite of Africa within hours and for less than 200 American dollars in comparison to having to buy a supercomputer for the work that we did on the NCI, then please, we're at the booth all week. And we are prepared to do live demos then, probably not now. And there's a huge amount of resources here that you can tap into. In particular, we're always available on Slack. So if you're interested, you can invite yourself to our Slack channel and say hello. That's it. All right. Thanks very much, Dave, for that. Do we have any questions from the audience to get going? No worries. I will start. So I think it's really great that you've been able to take all the success that you've had with Digital Earth Australia and start bringing that over to Africa. I wondered if you could talk a little more about sort of what was easy to bring over and what were some of the challenges or differences? Yeah. Okay. So there's some nuances to the answer of that. So a lot of the code just ran. There is a reason that Geoscience Australia believes that we need to process data for Australia differently to how ESA and USGS release that data. And I guess there were a few parts around equatorial Africa that we had some issues with some of that data. The other thing is that there were some workflows that had to be completely rewritten for AWS. And that sounds like it's a bad thing, but it's actually was from a very PBS focused within the national computational infrastructure to something that can just make use of fundamental things like message queues that are available in AWS. I mean, we are focused and committed to making sure that all of this infrastructure and code is all open source and will run anywhere. So we developed in Kubernetes and tried to do as much of the processing using lean into task which is hard, but was really rewarding in the end. So yeah, kind of answers your question, but touches again. No, that's great. Thanks. Do we have any questions from the audience? Come on. Got one from Alex. Who is your favorite employee? Don't worry about that. I don't have a serious question. Sorry, Dave. That's all right. You're my favorite employee. Alex, thank you. It's on record now. Do we have any other questions? All right. No worries. Well, could we thank Dave again, please? Thanks for here, everyone. Thanks again, Dave.
 Today, I'm here to talk to you about the Open Data Cube and working towards digital  earth.  As Kurt laid out in his presentation, there are two gold standards in open satellite data.  Those are the Landsat missions, Landsats 5, 7 and 8, and the Sentinel 2 or the Sentinel  missions.  But today, I'll be focusing on optical satellite data.  But rather than exploring the different bands that are available on each of the different  platforms, I'll just focus in on the six bands that were common to all five satellites when  we talk about Sentinel 2a and 2b.  So from 1986 onwards, we have 30-meter resolution capture from Landsat with a 16-day revisit.  So every pixel of the planet gets imaged every 16 days, and which means we have a tremendous  archive of optical data that is incredibly sparse.  From 2017 onwards, the second Sentinel 2 satellite was launched, and we now have 10-meter resolution  and a five-day revisit.  Combined, this is a fantastic way of being able to see our dynamic earth in action.  So this is an image of a flood from in northwest Queensland in February of this year.  This is the same area in October, so that's completely drought-ridden, no water.  As Kurt showed, this is some of the bushfires burning in New South Wales right now, and  with these six bands, we can see through the smoke, and we can see the firefronts burning  in the sky.  With these six bands, we can see unparalleled beauty of our planet, and we can do things  like classify water.  So that's what we can do with static imagery.  If you just go out and download a single image or see it through a web service, that's the  sort of thing that you can do.  But if you want to do more, if you want to get your hands dirty with this data and derive  new insights with it, then there's a tremendous amount of work that goes into actually acquiring  this data, author-rectifying it, calibrating it, and then making it ready for time series  analysis.  So it's not just a matter of one photo to another.  It's that every cell is ready to be interpreted and interrogated.  So a couple of years ago, Geoscience Australia worked with the NCI and CSIRO to build the  Australian Geoscience DataCube.  In addition to actually processing all of this data and co-locating this data on the  NCI, we set about creating an intuitive Python API, which is now known as the Open DataCube.  In a nutshell, the Open DataCube provides a Python X-array interface to this sparse  and rich data.  So given a user-defined query, we go back to a Postgres database.  We find all of the data sets that match your area of interest or any other attributes that  you're querying on.  Then the Open DataCube will group and stream the data back to you.  So depending on whether you're just browsing or you're trying to do a whole set of processing  on this data, this is all X-array, NumPy, and DASC enabled.  So it's meant to be as intuitive and as a tool for rapid development and rapid insight  gathering.  So this is a tool our remote sensing scientists use.  So that was a big paradigm shift in terms of empowering our scientific staff who already  had knowledge of Python but gave them the knowledge to go further as well as to develop  tools in concert with them.  So with these tools, we can create Cloud-free mosaics.  We can classify the ground cover using the joint remote sensing research program's fractional  cover and in this ternary image, we have the bright greens as healthy photosynthetic vegetation,  the blue as nonphotosynthetic vegetation, and red as bare soil.  And in this, this is a representation of how often water is identified in a particular  place.  We have the deep blue or purple, the top is where, well, it's ocean, so we always see  water there.  And then with these reds and yellows, there are things that have been imaged maybe 5%  or less of the time, so they're actually floods.  So this particular color scheme helps you see both where standing water is and always  has been but also where irregular inundations of water can occur.  And recently we did a lot of work into mapping out the intertidal regions around Australia  using Landsat.  So using a tidal model, you were able to identify all of the low tide images and contrast them  with the high tide images and develop a digital elevation model for this region of Australia.  So at the close of the Australian Geoscience data cube, or rather the transition to digital  earth Australia, we had all of this, we had petabytes of data located on the ANU supercomputer,  which was fantastic for us to use and other users of the ANU, but it wasn't particularly  accessible to our government partners and it wasn't particularly accessible to the public  at large.  So there were two driving goals of digital earth Australia.  So that was to apply remote sensing science and satellite information to aid government  decisions and policy making and to work with industry innovators to stimulate economic growth  for Australia using satellite imagery.  For us, the strategy was the same.  We needed to make the data accessible, we needed to make the software more usable and  we needed to work closely and alongside our stakeholders.  So today, all of the data that DEA produces is available from NCI through threads or through  Amazon web services via S3.  We are looking at projects at the moment to make this data available through Google Earth  Engine and Google Cloud Platform.  We have worked and invested heavily with the Terrier and Data61 team to enhance the Terrier  portal and to build a WMS interface on top of the open data cube.  So in this, we're seeing a early prototype of some of the graphing and time series analysis  that we intend to make more usable and more accessible to Kurt's dad.  Well, we'll keep working on it so that people can combine things like stream gauge data  or in this case, standing body, standing water body information alongside the imagery from  and to be able to visualize what's going on.  We have worked really hard with FrontierSI to make the Jupyter notebooks that our scientific  staff have developed in available to everyone.  So not only is the open data cube open source and are these notebooks available for people  to download.  This sandbox is available today.  So if anybody took part in the open data cube master class yesterday, you would have had  an opportunity to run on AWS hosted infrastructure running in Kubernetes, which you can go into  more detail later.  That basically allows users to go in and go out the data with Python and X array and  scipy and do whatever they want.  So last year, we participated with Murray Darling Basin Authority's Northern connectivity  event.  So this was a controlled release of water through the Murray Darling Basin to stimulate  the river itself.  It wasn't actually meant for farmers.  It was because this is parts of the river had completely dried out and that's vital  for the ecosystem that this all gets connected.  There was suspicions.  There was anticipation that not all of the water was necessarily going to make it all  the way through.  And a year later, we're in the news with satellite images question how private dams filled during  Murray Darling pumping in Vargo.  And actually, there's more controversial headlines that I could have put up, but probably get  in trouble.  So that's just one example of how we are working with other government agencies to improve  policymaking for Australians.  For the industry engagement, we also partnered with Frontier Si to work closely with private  all sectors of private industry within Australia to figure out what we could be doing, how  we could make this data more accessible, how we can make these insights more accessible,  what do people want.  So that was done through all of those metrics, which I won't read out.  And it culminated in the ambitious Digital Earth Australia Labs program.  So Labs is a grants program, grants, not grants.  And Sandbox is a Jupiter hub environment.  People do get these things mixed up.  But so we have had three successful lab entrants that were working through at the moment.  One of which is Sebo Labs.  So their particular Labs project is to investigate using AWS serverless to enhance their pasture  biomass products.  So this is an information system that allows farmers to figure out how healthy their pastures  and fields are so that they can do better rotation of their livestock and feed and be  more responsible and successful.  And to the culmination of a lot of this work has been, particularly this year, has been  to create this capability for Africa.  So we have, I wouldn't say partnered, we were very lucky that the United States Geological  Survey Organization gave us access to their collection to data early.  And we were able to, only for Landsat 8 at the moment, but it is anticipated that Landsat  5, 7, and 8 will all be available for the entire world at an analysis-ready processing level  from, I'll say June next year.  It's supposed to be earlier.  That's the conservative estimate.  But last week at the Geo-Ministerial event, we were able to show a cloud-free mosaic of  Africa, which was just one of the many different derived and decision-ready products that we  were able to produce and demonstrate.  So this is all still a work in progress.  This has its own sandbox if people want to go and play with this data.  Once again, this is available through Terrier.  And we are frequently being approached by other countries who want a data cube, want  to find out more about how they can build a data cube.  So if any of this is of any interest to you, so whether it's at the Australian data of  DEA and DEA Labs, or whether it's Open Data Cube or Digital Earth Africa, or if it's how  we used DASC to produce a cloud-free composite of Africa within hours and for less than 200  American dollars in comparison to having to buy a supercomputer for the work that we did  on the NCI, then please, we're at the booth all week.  And we are prepared to do live demos then, probably not now.  And there's a huge amount of resources here that you can tap into.  In particular, we're always available on Slack.  So if you're interested, you can invite yourself to our Slack channel and say hello.  That's it.  All right.  Thanks very much, Dave, for that.  Do we have any questions from the audience to get going?  No worries.  I will start.  So I think it's really great that you've been able to take all the success that you've  had with Digital Earth Australia and start bringing that over to Africa.  I wondered if you could talk a little more about sort of what was easy to bring over  and what were some of the challenges or differences?  Yeah.  Okay.  So there's some nuances to the answer of that.  So a lot of the code just ran.  There is a reason that Geoscience Australia believes that we need to process data for  Australia differently to how ESA and USGS release that data.  And I guess there were a few parts around equatorial Africa that we had some issues  with some of that data.  The other thing is that there were some workflows that had to be completely rewritten for AWS.  And that sounds like it's a bad thing, but it's actually was from a very PBS focused  within the national computational infrastructure to something that can just make use of fundamental  things like message queues that are available in AWS.  I mean, we are focused and committed to making sure that all of this infrastructure and code  is all open source and will run anywhere.  So we developed in Kubernetes and tried to do as much of the processing using lean into  task which is hard, but was really rewarding in the end.  So yeah, kind of answers your question, but touches again.  No, that's great.  Thanks.  Do we have any questions from the audience?  Come on.  Got one from Alex.  Who is your favorite employee?  Don't worry about that.  I don't have a serious question.  Sorry, Dave.  That's all right.  You're my favorite employee.  Alex, thank you.  It's on record now.  Do we have any other questions?  All right.  No worries.  Well, could we thank Dave again, please?  Thanks for here, everyone.  Thanks again, Dave.
David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_0_47.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_48_167.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_168_1991.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_1992_2231.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_2232_2435.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_2436_2795.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_2796_2939.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_2940_3059.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_3060_4655.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_4656_4727.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_4728_6503.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_6504_6647.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_6648_7127.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_7128_7979.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_7980_8579.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_8580_9983.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_9984_10427.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_10428_11603.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_11604_12791.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_12792_14243.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_14244_15491.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_15492_16259.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_16260_18311.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_18312_19607.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_19608_23579.png David_Gavin_-_The_Open_Data_Cube_and_working_towards_a_Digital_Ea_23580_23590.png
Today, I'm here to talk to you about the Open Data Cube and working towards digital earth. As Kurt laid out in his presentation, there are two gold standards in open satellite data. Those are the Landsat missions, Landsats 5, 7 and 8, and the Sentinel 2 or the Sentinel missions. But today, I'll be focusing on optical satellite data. But rather than exploring the different bands that are available on each of the different platforms, I'll just focus in on the six bands that were common to all five satellites when we talk about Sentinel 2a and 2b. So from 1986 onwards, we have 30-meter resolution capture from Landsat with a 16-day revisit. So every pixel of the planet gets imaged every 16 days, and which means we have a tremendous archive of optical data that is incredibly sparse. From 2017 onwards, the second Sentinel 2 satellite was launched, and we now have 10-meter resolution and a five-day revisit. Combined, this is a fantastic way of being able to see our dynamic earth in action. So this is an image of a flood from in northwest Queensland in February of this year. This is the same area in October, so that's completely drought-ridden, no water. As Kurt showed, this is some of the bushfires burning in New South Wales right now, and with these six bands, we can see through the smoke, and we can see the firefronts burning in the sky. With these six bands, we can see unparalleled beauty of our planet, and we can do things like classify water. So that's what we can do with static imagery. If you just go out and download a single image or see it through a web service, that's the sort of thing that you can do. But if you want to do more, if you want to get your hands dirty with this data and derive new insights with it, then there's a tremendous amount of work that goes into actually acquiring this data, author-rectifying it, calibrating it, and then making it ready for time series analysis. So it's not just a matter of one photo to another. It's that every cell is ready to be interpreted and interrogated. So a couple of years ago, Geoscience Australia worked with the NCI and CSIRO to build the Australian Geoscience DataCube. In addition to actually processing all of this data and co-locating this data on the NCI, we set about creating an intuitive Python API, which is now known as the Open DataCube. In a nutshell, the Open DataCube provides a Python X-array interface to this sparse and rich data. So given a user-defined query, we go back to a Postgres database. We find all of the data sets that match your area of interest or any other attributes that you're querying on. Then the Open DataCube will group and stream the data back to you. So depending on whether you're just browsing or you're trying to do a whole set of processing on this data, this is all X-array, NumPy, and DASC enabled. So it's meant to be as intuitive and as a tool for rapid development and rapid insight gathering. So this is a tool our remote sensing scientists use. So that was a big paradigm shift in terms of empowering our scientific staff who already had knowledge of Python but gave them the knowledge to go further as well as to develop tools in concert with them. So with these tools, we can create Cloud-free mosaics. We can classify the ground cover using the joint remote sensing research program's fractional cover and in this ternary image, we have the bright greens as healthy photosynthetic vegetation, the blue as nonphotosynthetic vegetation, and red as bare soil. And in this, this is a representation of how often water is identified in a particular place. We have the deep blue or purple, the top is where, well, it's ocean, so we always see water there. And then with these reds and yellows, there are things that have been imaged maybe 5% or less of the time, so they're actually floods. So this particular color scheme helps you see both where standing water is and always has been but also where irregular inundations of water can occur. And recently we did a lot of work into mapping out the intertidal regions around Australia using Landsat. So using a tidal model, you were able to identify all of the low tide images and contrast them with the high tide images and develop a digital elevation model for this region of Australia. So at the close of the Australian Geoscience data cube, or rather the transition to digital earth Australia, we had all of this, we had petabytes of data located on the ANU supercomputer, which was fantastic for us to use and other users of the ANU, but it wasn't particularly accessible to our government partners and it wasn't particularly accessible to the public at large. So there were two driving goals of digital earth Australia. So that was to apply remote sensing science and satellite information to aid government decisions and policy making and to work with industry innovators to stimulate economic growth for Australia using satellite imagery. For us, the strategy was the same. We needed to make the data accessible, we needed to make the software more usable and we needed to work closely and alongside our stakeholders. So today, all of the data that DEA produces is available from NCI through threads or through Amazon web services via S3. We are looking at projects at the moment to make this data available through Google Earth Engine and Google Cloud Platform. We have worked and invested heavily with the Terrier and Data61 team to enhance the Terrier portal and to build a WMS interface on top of the open data cube. So in this, we're seeing a early prototype of some of the graphing and time series analysis that we intend to make more usable and more accessible to Kurt's dad. Well, we'll keep working on it so that people can combine things like stream gauge data or in this case, standing body, standing water body information alongside the imagery from and to be able to visualize what's going on. We have worked really hard with FrontierSI to make the Jupyter notebooks that our scientific staff have developed in available to everyone. So not only is the open data cube open source and are these notebooks available for people to download. This sandbox is available today. So if anybody took part in the open data cube master class yesterday, you would have had an opportunity to run on AWS hosted infrastructure running in Kubernetes, which you can go into more detail later. That basically allows users to go in and go out the data with Python and X array and scipy and do whatever they want. So last year, we participated with Murray Darling Basin Authority's Northern connectivity event. So this was a controlled release of water through the Murray Darling Basin to stimulate the river itself. It wasn't actually meant for farmers. It was because this is parts of the river had completely dried out and that's vital for the ecosystem that this all gets connected. There was suspicions. There was anticipation that not all of the water was necessarily going to make it all the way through. And a year later, we're in the news with satellite images question how private dams filled during Murray Darling pumping in Vargo. And actually, there's more controversial headlines that I could have put up, but probably get in trouble. So that's just one example of how we are working with other government agencies to improve policymaking for Australians. For the industry engagement, we also partnered with Frontier Si to work closely with private all sectors of private industry within Australia to figure out what we could be doing, how we could make this data more accessible, how we can make these insights more accessible, what do people want. So that was done through all of those metrics, which I won't read out. And it culminated in the ambitious Digital Earth Australia Labs program. So Labs is a grants program, grants, not grants. And Sandbox is a Jupiter hub environment. People do get these things mixed up. But so we have had three successful lab entrants that were working through at the moment. One of which is Sebo Labs. So their particular Labs project is to investigate using AWS serverless to enhance their pasture biomass products. So this is an information system that allows farmers to figure out how healthy their pastures and fields are so that they can do better rotation of their livestock and feed and be more responsible and successful. And to the culmination of a lot of this work has been, particularly this year, has been to create this capability for Africa. So we have, I wouldn't say partnered, we were very lucky that the United States Geological Survey Organization gave us access to their collection to data early. And we were able to, only for Landsat 8 at the moment, but it is anticipated that Landsat 5, 7, and 8 will all be available for the entire world at an analysis-ready processing level from, I'll say June next year. It's supposed to be earlier. That's the conservative estimate. But last week at the Geo-Ministerial event, we were able to show a cloud-free mosaic of Africa, which was just one of the many different derived and decision-ready products that we were able to produce and demonstrate. So this is all still a work in progress. This has its own sandbox if people want to go and play with this data. Once again, this is available through Terrier. And we are frequently being approached by other countries who want a data cube, want to find out more about how they can build a data cube. So if any of this is of any interest to you, so whether it's at the Australian data of DEA and DEA Labs, or whether it's Open Data Cube or Digital Earth Africa, or if it's how we used DASC to produce a cloud-free composite of Africa within hours and for less than 200 American dollars in comparison to having to buy a supercomputer for the work that we did on the NCI, then please, we're at the booth all week. And we are prepared to do live demos then, probably not now. And there's a huge amount of resources here that you can tap into. In particular, we're always available on Slack. So if you're interested, you can invite yourself to our Slack channel and say hello. That's it. All right. Thanks very much, Dave, for that. Do we have any questions from the audience to get going? No worries. I will start. So I think it's really great that you've been able to take all the success that you've had with Digital Earth Australia and start bringing that over to Africa. I wondered if you could talk a little more about sort of what was easy to bring over and what were some of the challenges or differences? Yeah. Okay. So there's some nuances to the answer of that. So a lot of the code just ran. There is a reason that Geoscience Australia believes that we need to process data for Australia differently to how ESA and USGS release that data. And I guess there were a few parts around equatorial Africa that we had some issues with some of that data. The other thing is that there were some workflows that had to be completely rewritten for AWS. And that sounds like it's a bad thing, but it's actually was from a very PBS focused within the national computational infrastructure to something that can just make use of fundamental things like message queues that are available in AWS. I mean, we are focused and committed to making sure that all of this infrastructure and code is all open source and will run anywhere. So we developed in Kubernetes and tried to do as much of the processing using lean into task which is hard, but was really rewarding in the end. So yeah, kind of answers your question, but touches again. No, that's great. Thanks. Do we have any questions from the audience? Come on. Got one from Alex. Who is your favorite employee? Don't worry about that. I don't have a serious question. Sorry, Dave. That's all right. You're my favorite employee. Alex, thank you. It's on record now. Do we have any other questions? All right. No worries. Well, could we thank Dave again, please? Thanks for here, everyone. Thanks again, Dave.
talk about Sentinel 2a and 2b. So from 1986 onwards, the second Sentinel 2 satellite was New South Wales right now, and with these six Australia. So at the close of the Australian only is the open data cube open source and are the way through. And a year later, we're in the