1 00:00:00,000 --> 00:00:06,179 Good morning everyone and welcome and we're welcome to all who are joining us 2 00:00:06,179 --> 00:00:11,400 today for this discussion about harnessing data from within and beyond. 3 00:00:11,400 --> 00:00:16,080 Honestly whenever we're talking about data we all know that data is the new 4 00:00:16,080 --> 00:00:22,160 currency for today's era and we're flooded with a lot of information and a 5 00:00:22,160 --> 00:00:27,280 lot of data around us and if we're talking about data there are a lot of 6 00:00:27,280 --> 00:00:32,920 questions that come to our mind like what data is benefit to us and what are 7 00:00:32,920 --> 00:00:38,240 the challenges and how we can utilize it to benefit humanity. So to start this 8 00:00:38,240 --> 00:00:42,799 conversation within this panel I would like to ask you Miss Yana like what 9 00:00:42,799 --> 00:00:47,640 kind of data is really important among all the data that we do have and how we 10 00:00:47,640 --> 00:00:54,439 can share this data effectively. Good morning everyone it's really nice to be 11 00:00:54,479 --> 00:00:59,159 here and and to follow this opening remarks if I can just deviate a little 12 00:00:59,159 --> 00:01:05,879 bit is a tough call because I think Her Excellency managed in this most succinct 13 00:01:05,879 --> 00:01:10,120 eloquent talk to string all the important elements into this beautiful 14 00:01:10,120 --> 00:01:15,959 strand so it's a bit she's pretty much made the case for all of us so I'll try 15 00:01:15,959 --> 00:01:22,560 to fill perhaps some of the intervals that she left open for us to talk about. 16 00:01:22,560 --> 00:01:27,760 So within the group on earth observation as the name suggests we really 17 00:01:27,760 --> 00:01:34,640 think of data as encompassing of all kind of data that comes from varieties 18 00:01:34,640 --> 00:01:38,520 of sources that give us information about the earth and the earth system so 19 00:01:38,520 --> 00:01:44,400 we when we talk about data we are thinking about observations of physical 20 00:01:44,400 --> 00:01:49,560 chemical biological but also socioeconomic variables and observing 21 00:01:49,560 --> 00:01:56,719 their change over time in order to construct our understanding of what goes 22 00:01:56,719 --> 00:02:06,180 on on our mother planet earth but data in itself is just a raw material it's 23 00:02:06,180 --> 00:02:13,120 just raw commodity it as Her Excellency pointed out its real value is realized 24 00:02:13,120 --> 00:02:20,520 when it's processed when it's analyzed when insights are generated packaged in 25 00:02:20,520 --> 00:02:24,840 the kind of format that can actually deliver actionable information to any 26 00:02:24,840 --> 00:02:32,200 decision-makers be there public entities or businesses or citizens the thing 27 00:02:32,200 --> 00:02:39,680 about data it's just like in that symphony to for it to make sense we do 28 00:02:39,760 --> 00:02:45,080 need all of the elements to play their part but they all need to be connected 29 00:02:45,080 --> 00:02:50,920 to give us that holistic view of the question that we're trying to answer and 30 00:02:50,920 --> 00:02:56,240 one important thing that makes it happen and to be shared across communities and 31 00:02:56,240 --> 00:03:04,439 societies is something as boring as protocols and standards so it's really 32 00:03:04,439 --> 00:03:09,800 critical that the entities whether it is data providing organizations who 33 00:03:09,800 --> 00:03:16,039 share their original source data or if it's aggregators data aggregators like 34 00:03:16,039 --> 00:03:23,079 Amazon and Microsoft planetary computer Google they are all using those very 35 00:03:23,079 --> 00:03:31,039 critical standard based protocols API's which allow them this pieces of the 36 00:03:31,039 --> 00:03:35,799 orchestra to actually communicate and spread all the data and the information 37 00:03:35,799 --> 00:03:40,680 they provide throughout our global ecosystem and then of course come a 38 00:03:40,680 --> 00:03:47,199 whole myriad of other platforms which are chosen by the user community 39 00:03:47,199 --> 00:03:53,280 themselves from national data sharing infrastructures to regional continental 40 00:03:53,280 --> 00:03:58,199 scale like last year I believe there was a Middle East North Africa big data 41 00:03:58,359 --> 00:04:06,839 regional how blanched to again to global scale data platforms and from our side I 42 00:04:06,839 --> 00:04:12,079 think I would just say where the investments into this data are really 43 00:04:12,079 --> 00:04:18,199 being realized time and time again is when the data that becomes intelligence 44 00:04:18,199 --> 00:04:23,839 and then becomes knowledge through its use and application gets actually shared 45 00:04:24,039 --> 00:04:29,719 globally because it's that experience of having applied that data in decision 46 00:04:29,719 --> 00:04:34,199 making processes that generates the learning and then that learning when 47 00:04:34,199 --> 00:04:40,319 shared within the global community can indeed become the tool of empowerment so 48 00:04:40,319 --> 00:04:49,559 all data matters but data alone doesn't matter it's when data gets used as tools 49 00:04:49,759 --> 00:04:54,839 that provide intelligence and insights generate knowledge and learning which 50 00:04:54,839 --> 00:05:02,600 then is shared is when we really realize the full potential of data but to do 51 00:05:02,600 --> 00:05:07,240 that we have to take care of those boring little pesky things like standards 52 00:05:07,240 --> 00:05:15,959 and protocols for your insight so for us to get valuable data as even her 53 00:05:16,000 --> 00:05:19,599 excellency mentioned it gets back to the basics of collection of data 54 00:05:19,599 --> 00:05:23,919 professor talked to us more about like what are the challenges and the 55 00:05:23,919 --> 00:05:31,919 limitations when it comes to collection of data and even using it I do it like 56 00:05:31,919 --> 00:05:36,000 this so that I can also look to the left and to the right so good morning 57 00:05:36,000 --> 00:05:42,479 everybody and I must join Iana it was amazing how well the minister collected 58 00:05:42,480 --> 00:05:50,560 everything in one strand so what can be at first of all yes data are absolutely 59 00:05:50,560 --> 00:05:56,720 necessary and data collection is necessary but the first question is which 60 00:05:56,720 --> 00:06:03,439 data which data do we need which data do we want so that's the first question to 61 00:06:03,439 --> 00:06:09,680 define because we can collect whatever we want it wouldn't give the result if 62 00:06:09,680 --> 00:06:16,480 you would not have the right data and it has to be not only the right data it 63 00:06:16,480 --> 00:06:22,759 has to be the quality of the data so you need quality checks you need to define 64 00:06:22,759 --> 00:06:27,840 which quality the data have to have have to have and that's already the first 65 00:06:27,840 --> 00:06:36,399 challenge the second challenge is you cannot store all data you have to 66 00:06:36,399 --> 00:06:45,799 reduce the data so how to reduce and there comes the word of bias what the 67 00:06:45,799 --> 00:06:52,000 minister said that everybody has a bias also in research in research that can 68 00:06:52,000 --> 00:06:58,039 also be an unconscious bias because you don't know what you really need so that 69 00:06:58,040 --> 00:07:06,680 that's an intrinsic bias to some extent so you have to reduce without having the 70 00:07:06,680 --> 00:07:12,960 risk to reduce it by things which you need so it's to my mind it's an iterative 71 00:07:12,960 --> 00:07:19,480 process also you collect data you learn to use these data and then you need an 72 00:07:19,480 --> 00:07:25,240 iteration because you might have new info you will have new information 73 00:07:25,480 --> 00:07:30,720 definitely and with this new information you have an iterative process the next 74 00:07:30,720 --> 00:07:37,199 challenges you have then to run through the data again maybe to collect new data 75 00:07:37,199 --> 00:07:41,840 and to run through the old data again and there's a challenge which is called 76 00:07:41,840 --> 00:07:49,840 energy because it all needs power so you better invest a lot of thinking in order 77 00:07:49,839 --> 00:07:55,399 to reduce the amount of power because if you need too much power you can't 78 00:07:55,399 --> 00:08:04,599 reanalyze the data so these are all the challenges and then comes the biggest 79 00:08:04,599 --> 00:08:14,679 challenge you have to transform the data into knowledge and maybe that's that's 80 00:08:14,720 --> 00:08:20,400 really the biggest challenge to do that because you can collect whatever you 81 00:08:20,400 --> 00:08:26,639 want you must use it you must use it in order to bring us forward and there you 82 00:08:26,639 --> 00:08:30,600 have to have an idea what you want to find in which direction not exactly what 83 00:08:30,600 --> 00:08:36,560 because that would be already a bias so in which direction do you want to do 84 00:08:36,560 --> 00:08:42,720 your research and again there you are then an iterative process and again what 85 00:08:42,759 --> 00:08:46,759 Jana said you need to share it and you need to do it with others together you 86 00:08:46,759 --> 00:08:53,000 alone you cannot do that so I think these are the challenges which you can 87 00:08:53,000 --> 00:08:59,000 master once you know the direction where your goal is lying but you have to keep 88 00:08:59,000 --> 00:09:05,240 in mind you need a lot of preparatory time a lot of iterations and I think a 89 00:09:05,240 --> 00:09:10,240 lot of ingenuity in particular and that's another challenge which I just 90 00:09:10,240 --> 00:09:16,279 forgot if you want to make a disruptive change how can you define the 91 00:09:16,279 --> 00:09:22,720 disruption before and so again I think one needs an iteration there in order 92 00:09:22,720 --> 00:09:29,680 to get an interrupt disruptive change so there are a lot of challenges but you 93 00:09:29,680 --> 00:09:39,039 can master it and I think that's that's a positive end of this point so always 94 00:09:39,079 --> 00:09:42,599 with the opposite of challenges there is the other case of opportunities and I 95 00:09:42,599 --> 00:09:49,039 believe like technology can play a great role in that mr. Clint how can we 96 00:09:49,039 --> 00:09:53,919 utilize really technology to facilitate data usage like professor is saying 97 00:09:53,919 --> 00:09:58,559 like that we do have to revise the data that we want as we create more 98 00:09:58,559 --> 00:10:04,919 understanding so how can we meet these kind of demands of different data using 99 00:10:04,959 --> 00:10:09,879 the technology that we do have thank you very much you know one of the things 100 00:10:09,879 --> 00:10:16,959 that's that's happening is change like change is just a part of how we live and 101 00:10:16,959 --> 00:10:23,159 and how we work and this evolution of the the technology that I've dedicated 102 00:10:23,159 --> 00:10:30,279 my life to is GIS has has undergone enormous transformation in these last 103 00:10:30,319 --> 00:10:35,959 few years the internet has has actually changed everything the interesting thing 104 00:10:35,959 --> 00:10:43,519 about geographic data is everything happens somewhere so every bit of 105 00:10:43,519 --> 00:10:49,399 information in the world typically can be geo located somewhere on the planet 106 00:10:49,399 --> 00:10:54,839 or some other planet or moon depending on what the context of the problem is 107 00:10:55,680 --> 00:11:01,840 and the idea that you can bring different sets of data together unforeseen in the 108 00:11:01,840 --> 00:11:07,120 past is really kind of a big breakthrough for what's going on I was 109 00:11:07,120 --> 00:11:13,720 just thinking last night about some of the things we've seen in this new web GIS 110 00:11:13,720 --> 00:11:21,440 pattern for example Landsat 8 has a heat sensor and that heat sensor can be used 111 00:11:21,640 --> 00:11:26,640 to make maps of cities that show you where the warm parts of the cities are 112 00:11:26,640 --> 00:11:33,960 places where trees could be planted and places that have nice shade around them 113 00:11:33,960 --> 00:11:39,440 that's that's kind of amazing I saw it from my hometown last fall like like 114 00:11:39,440 --> 00:11:45,120 that idea of that information coming to life was was really new the other thing 115 00:11:45,120 --> 00:11:50,799 that we see a lot of like if you look at the European Space Agency and and 116 00:11:50,799 --> 00:12:01,600 their work their sentinel missions are just stunningly capable in the past six 117 00:12:01,600 --> 00:12:09,360 years the lands sorry the sentinel two missions have produced amazing 118 00:12:09,360 --> 00:12:15,080 information they have a revisit rate every 10 meter pixel on the planet is 119 00:12:15,080 --> 00:12:22,680 revisited every five days and the work that was done by a company called impact 120 00:12:22,680 --> 00:12:31,120 observatory was to use AI and machine learning to take about I think it's 121 00:12:31,120 --> 00:12:39,240 around 24,000 sample plots around the world and using machine learning they 122 00:12:39,240 --> 00:12:46,320 turned that into over a million sample plots and imagine this the entire year's 123 00:12:46,320 --> 00:12:55,000 worth of imagery all of that content for each year from 2017 through 2022 now have 124 00:12:55,000 --> 00:13:02,039 been used to create an annual land cover map at 10 meter resolution and when you 125 00:13:02,039 --> 00:13:09,519 start to think about how we do kind of continental and and and global planning 126 00:13:09,519 --> 00:13:14,319 of things how does that information roll up that's one of the interesting things 127 00:13:14,319 --> 00:13:21,480 about raster data or imagery data it's you know there are these cells that are 128 00:13:21,480 --> 00:13:26,519 used where where cells can nested other cells in other words you can take a data 129 00:13:26,519 --> 00:13:34,799 cube of lots of layers like in the United States all observations for species at risk 130 00:13:34,799 --> 00:13:42,120 have been collected for over 25 years have been turned into a data cube of about 2700 131 00:13:42,120 --> 00:13:49,799 separate layers one for each species about where it had been located and that... 132 00:13:49,799 --> 00:13:58,799 used to generate a map of probability of occurrence not for all species but for eac... 133 00:13:58,799 --> 00:14:07,799 species I mean it's kind of just just enormous breakthroughs and what can happen... 134 00:14:07,799 --> 00:14:15,639 the patterns that's been emerging have have been these this kind of community content ... 135 00:14:15,639 --> 00:14:23,199 started to build something we call the living atlas of the world and it's it's this... 136 00:14:23,199 --> 00:14:33,159 of I'll say curated content the best available content just imagine about all... 137 00:14:33,159 --> 00:14:42,480 imagery and raster and things like that population statistics transportation healt... 138 00:14:42,519 --> 00:14:49,600 there's about 10,000 layers that we've been curating we add a few layers every month t... 139 00:14:49,600 --> 00:14:58,320 continuously keep it updated what it represents is it represents the work of th... 140 00:14:58,320 --> 00:15:06,080 community all of their work is coming to life and that idea of data becoming available i... 141 00:15:06,080 --> 00:15:13,520 another one about well how do you synthesize that data and put it into a form where peo... 142 00:15:13,520 --> 00:15:21,400 computation with it and drive real answers to really pressing problems and I think I think 143 00:15:21,400 --> 00:15:29,920 that's a big lesson for for us have always been just the amazing work like you look a... 144 00:15:29,919 --> 00:15:37,079 inspire you know groups like that have done just a wonderful job at kind of building that 145 00:15:37,079 --> 00:15:45,479 ethic about information sharing across our community so I'm really excited about what... 146 00:15:45,479 --> 00:15:52,360 GIS community can offer to things like the sustainable development goals that's honestly 147 00:15:52,360 --> 00:15:59,439 great combining multiple layers of information to reach the whole benefit to... 148 00:15:59,440 --> 00:16:04,400 humanity is the ultimate goal I would really like to hear from each one of you like fro... 149 00:16:04,400 --> 00:16:11,920 experiences what are some examples of how data were used to ultimately benefit human... 150 00:16:11,920 --> 00:16:21,080 can start with you Missyana. I have to pick a favorite and nobody likes to do that right no 151 00:16:21,080 --> 00:16:31,200 favorites but we can talk about some life-saving sort of scenarios where and ag... 152 00:16:31,200 --> 00:16:38,120 global partnership really multilateral partnership where I think the real value i... 153 00:16:38,120 --> 00:16:45,560 of the different communities from around the world from research science to technology and 154 00:16:45,559 --> 00:16:53,919 policy to actually co-develop and co-design solutions and throughout that process buil... 155 00:16:53,919 --> 00:16:59,079 which by the way one of the things that Her Excellency was talking about is how do we... 156 00:16:59,079 --> 00:17:04,399 on how we address this and let's just be clear governments don't really don't love ... 157 00:17:04,399 --> 00:17:12,240 risk so it is really this exercise of building the social capital that sort of... 158 00:17:12,240 --> 00:17:18,319 of trust and confidence that sort of tells us you know what go ahead and innovate and tr... 159 00:17:18,319 --> 00:17:27,200 new so through this multilateral collaboration we've actually developed a... 160 00:17:27,200 --> 00:17:36,880 some cases have allowed countries to anticipate significant risks like in the c... 161 00:17:37,480 --> 00:17:45,160 the prime minister's office wanted to create a social safety net program and by... 162 00:17:45,160 --> 00:17:54,560 global risk finance facility sort of looking for ways to create national level warning... 163 00:17:54,560 --> 00:18:01,280 trigger the warning early enough that the government could then receive the payouts... 164 00:18:01,279 --> 00:18:08,399 this insurance to provide proactive assistance with the population so through ... 165 00:18:08,399 --> 00:18:15,879 worked with the government of Uganda to develop their national level crop monitor... 166 00:18:15,879 --> 00:18:23,480 system which using the information about the different types of variables triggered three 167 00:18:23,480 --> 00:18:29,960 month warning to let them know that in that season they would not be able to expect th... 168 00:18:29,960 --> 00:18:37,079 level of crop yields and that allowed them to trigger the insurance basically and receiv... 169 00:18:37,079 --> 00:18:43,480 payouts which then they used to provide alternative means of livelihoods to the... 170 00:18:43,480 --> 00:18:50,319 depended on the crops which are rainfed which they wouldn't have so by being able to tak... 171 00:18:50,319 --> 00:18:57,200 anticipatory action they really saved a lot in terms of a cost that they would have ha... 172 00:18:57,200 --> 00:19:02,480 then to subsidize things after the event and really a major disruption to the lives and 173 00:19:02,480 --> 00:19:12,160 livelihoods of the people so a number of such programs that we've put in place that our... 174 00:19:12,160 --> 00:19:18,200 states do like to leverage because they represent sort of that international... 175 00:19:18,200 --> 00:19:24,000 output that as Clint was saying takes the best of the best and puts in the context t... 176 00:19:24,000 --> 00:19:30,039 trusted by the national entities. That's honestly very powerful. Professor from you... 177 00:19:30,039 --> 00:19:37,359 give us an example of from the different set of data that you deal with how data was us... 178 00:19:37,359 --> 00:19:46,559 benefit humanity. Well I think at the moment you can discuss if it was good for the... 179 00:19:46,559 --> 00:19:55,319 not so good but 1989 the World Wide Web was born at CERN so that was of course fantastic 180 00:19:55,319 --> 00:20:04,119 achievement I'm no longer so sure if this is so it's I think it's good for humanity but... 181 00:20:04,119 --> 00:20:11,759 other hand it also is a challenge for humanity to keep it under control yeah and... 182 00:20:11,759 --> 00:20:18,480 to humanity free to the society business is why we have one web and not many different... 183 00:20:18,480 --> 00:20:25,680 which you have to pay etc so that helps of course to communicate to exchange informat... 184 00:20:25,680 --> 00:20:31,879 it also helps to my mind at least to bring people together yeah so I think it's a big... 185 00:20:31,879 --> 00:20:41,200 The other point is that we have learned to work together globally in a similar way as... 186 00:20:41,240 --> 00:20:48,880 described it and I think there is essentially nothing in the world we can solve ourselves 187 00:20:48,880 --> 00:20:57,640 alone we need to work together for me personally the best example of working... 188 00:20:57,640 --> 00:21:04,200 data was the discovery of the Higgs boson at the Large Hadron Collider of course so I h... 189 00:21:04,200 --> 00:21:20,000 bring in a little bit of my research. I have a few thoughts about this. Hello there it ... 190 00:21:20,000 --> 00:21:25,920 so this is an interesting time with what's going on and it's just a lot of thoughts... 191 00:21:25,920 --> 00:21:35,160 my head like the development of the internet and what it became is pretty magnificent.... 192 00:21:35,160 --> 00:21:44,460 interesting across our communities is that sharing ethic is I think bred into everybody 193 00:21:44,460 --> 00:21:50,960 who applies GIS because we're all so hungry for information and there's always at leas... 194 00:21:50,960 --> 00:21:58,920 information that we don't have and we don't have access to it unless we know somebody ... 195 00:21:58,920 --> 00:22:06,039 we can identify what that source is and it's just so beautiful when we can bring those... 196 00:22:06,039 --> 00:22:12,680 together that's the neat thing about GIS is because everything's geo-referenced it's just 197 00:22:12,680 --> 00:22:19,840 this idea that layers can be brought together in really interesting ways and some problems 198 00:22:20,000 --> 00:22:27,159 that you would never think about being kind of addressed with this technology and... 199 00:22:27,159 --> 00:22:37,959 like that like when the pandemic started you know the outbreaks of COVID had started in... 200 00:22:37,959 --> 00:22:46,079 was a young man had just started his graduate program at Johns Hopkins University and he... 201 00:22:46,119 --> 00:22:58,480 to his professor and he said my mother and father live in China right in Wuhan. He sa... 202 00:22:58,480 --> 00:23:05,639 show my parents what's going on with COVID and this was at a time where nobody really... 203 00:23:05,639 --> 00:23:15,519 anything but the first COVID dashboard his professor Lauren Gardner his name is Enshe... 204 00:23:15,519 --> 00:23:23,039 of them started the Johns Hopkins dashboard and they had it up and running in two days 205 00:23:23,039 --> 00:23:32,920 literally two days and of course it started getting hacked the third day something lik... 206 00:23:32,920 --> 00:23:41,480 but over time that thing grew to be this magnificent system and and it's all it's j... 207 00:23:41,480 --> 00:23:48,200 principles that that we all learn about like they could they could just scrape content ... 208 00:23:48,200 --> 00:23:54,200 web that's the first way they did it eventually they went to really robust meth... 209 00:23:54,200 --> 00:24:01,360 the data and and and things like that but to build the application that application tha... 210 00:24:01,360 --> 00:24:09,079 dashboard and reporting on things is one of the universal applications across all fiel... 211 00:24:09,079 --> 00:24:16,079 so important and interestingly that dashboard got used by the end of the first year by t... 212 00:24:16,079 --> 00:24:26,199 2020 had been viewed two trillion times it's kind of amazing you know for this little... 213 00:24:26,199 --> 00:24:33,839 dashboard to do that and what's interesting about it is the World Health Organization ... 214 00:24:34,839 --> 00:24:42,039 CDC those scientists were really focused on doing their research but it never occurred... 215 00:24:42,039 --> 00:24:50,199 share the data with the public it's just not what scientists do but these GIS people ca... 216 00:24:50,199 --> 00:24:59,439 created this way to reach everyone in the world and and it kind of changed I know wh... 217 00:24:59,480 --> 00:25:07,200 the United States we were kind of brain dead about what to do you know politicians real... 218 00:25:07,200 --> 00:25:14,920 answers and communication was just horrible but but you know there was just this big... 219 00:25:14,920 --> 00:25:23,759 so I think there are some examples of of these really remarkable events about about... 220 00:25:23,759 --> 00:25:32,000 technology about how open data can be brought to life and be used and and make the world... 221 00:25:32,000 --> 00:25:41,039 place like like turning to the SDGs I think that's a really big opportunity for us and... 222 00:25:41,039 --> 00:25:47,200 to be at this meeting to talk about that you brought a really great important topic whi... 223 00:25:47,200 --> 00:25:52,799 about sharing research results and information like this topic is very close ... 224 00:25:52,839 --> 00:25:59,639 I work in science as well professor you as well like I want your insight like when it... 225 00:25:59,639 --> 00:26:06,559 research results visibility especially like to stakeholders to government entities to key 226 00:26:06,559 --> 00:26:19,240 decision-maker what is the status that we see right now how important it is absolutely it's 227 00:26:19,279 --> 00:26:29,880 absolutely important but it's not only a one way it's a two-way street you have to sear... 228 00:26:29,880 --> 00:26:37,200 the contact to the decision makers but the decision makers also have to search and us... 229 00:26:37,200 --> 00:26:45,200 with with researchers with the people at the same time you have to keep your independen... 230 00:26:45,200 --> 00:26:52,039 you have to define clearly the boundaries between science scientists decision makers... 231 00:26:52,039 --> 00:26:58,240 there has have to be clear boundaries because if you don't respect these boundaries you ... 232 00:26:58,240 --> 00:27:08,160 intermixing and at that moment you introduce immediately bias and other problems okay s... 233 00:27:08,160 --> 00:27:22,560 in theory the biggest problem here is the language because it if you use certain words 234 00:27:22,560 --> 00:27:31,840 a scientist might understand a different con might use it put a different content into... 235 00:27:31,839 --> 00:27:42,039 and even in science take social science and natural science it's not so easy to... 236 00:27:42,039 --> 00:27:50,359 each other so that one one talks about the same content yeah it's amazing but if you ... 237 00:27:50,359 --> 00:28:01,039 you realize that you mean the same and that enriches the whole research that I personally 238 00:28:01,039 --> 00:28:10,559 found this amazing how this can work it's more difficult with politicians I learned... 239 00:28:10,559 --> 00:28:22,519 hard way the expressing something so that the politicians within their comfort zone... 240 00:28:22,519 --> 00:28:29,759 what we mean and the other way around so if you talk to politicians for example and th... 241 00:28:29,759 --> 00:28:35,159 a question you have to discuss it first the content of the question yeah because you... 242 00:28:35,160 --> 00:28:40,200 understand something different in the question than the point and then your... 243 00:28:40,200 --> 00:28:45,279 have to develop the question and you have to develop the question in such a way that yo... 244 00:28:45,279 --> 00:28:53,920 answer them it scientifically yeah because what you have to do as a scientist you hav... 245 00:28:53,920 --> 00:29:01,600 scientifically so again and then in trying to answer that question in the course of the... 246 00:29:01,679 --> 00:29:07,839 you have to interact with the politicians again because what you find out might chan... 247 00:29:07,839 --> 00:29:13,959 bit the content of the question but here you are again at the boundary between a clear... 248 00:29:13,959 --> 00:29:23,000 between the science and the politicians I find it an amazing an amazing field of yea... 249 00:29:23,880 --> 00:29:33,079 of slippery surface I mean it's clear but it is absolutely vital that politicians... 250 00:29:33,079 --> 00:29:41,240 talk and sometimes follow or usually follow the science because no decision is a good 251 00:29:41,240 --> 00:29:51,440 decision without basis in from science and research that has to be in that's great... 252 00:29:51,480 --> 00:29:57,400 talked about having conversations between different people with different... 253 00:29:57,400 --> 00:30:03,279 Miss Yanna you have such experience with having different partners so give me your... 254 00:30:03,279 --> 00:30:10,759 the importance of having diverse yet open conversations between different entities... 255 00:30:10,759 --> 00:30:19,240 when it comes to the advancement of space-based data you know so far we've bee... 256 00:30:19,279 --> 00:30:26,960 rosy picture of data and what it's powering and empowering but to be quite honest we'r... 257 00:30:26,960 --> 00:30:32,880 far from the ideal we have delusion abundance of data we have actually abundance of... 258 00:30:32,880 --> 00:30:39,279 but we also have significant areas of gap gaps of data and gaps of knowledge and gap... 259 00:30:39,279 --> 00:30:48,160 empowerment and I think we need to be honest with ourselves that we still have some... 260 00:30:48,160 --> 00:30:54,040 especially when it comes to actually the most vulnerable communities like the large ocea... 261 00:30:54,040 --> 00:31:00,480 or the small island development states or the indigenous communities in the Amazon speak... 262 00:31:00,480 --> 00:31:06,040 the large ocean states in particular and small island states I was in the Pacific l... 263 00:31:06,040 --> 00:31:15,759 and the global products don't actually provide quality information over their reg... 264 00:31:15,759 --> 00:31:22,200 important for us to recognize this and then continue to evolve our programs in a way that 265 00:31:22,200 --> 00:31:28,240 bring that inclusivity that Her Excellency talked about and involve these groups and... 266 00:31:28,240 --> 00:31:38,200 in our efforts of measuring monitoring mapping so that this dark spots can actual... 267 00:31:38,200 --> 00:31:45,279 science with knowledge with information Clint talked about the living atlas and the... 268 00:31:45,279 --> 00:31:52,720 observatories land cover map you know there are several such products there are also... 269 00:31:52,720 --> 00:32:00,240 just take wetland monitoring programs 30 over 30 of them worldwide different institution... 270 00:32:00,240 --> 00:32:10,200 wetlands but countries who are parties to the Ramsar Convention on wetlands a great numb... 271 00:32:10,200 --> 00:32:16,799 them still do not have information on wetlands to create their national wetlands... 272 00:32:16,799 --> 00:32:24,359 tells you that the system is not really perfect so we have in fact created an... 273 00:32:24,359 --> 00:32:33,039 we have on one hand proliferation of product solutions but also fragmentation and so th... 274 00:32:33,039 --> 00:32:40,719 opportunity that we see in geo is actually to act as a brokers and neutral convener of a... 275 00:32:40,719 --> 00:32:46,319 different actors and entities who've done very very good work but also identify the... 276 00:32:46,319 --> 00:32:54,159 that haven't been addressed by this work and bring them together and create a more... 277 00:32:54,160 --> 00:33:01,440 of a new solution that pieces this pieces together and provides a more holistic kind... 278 00:33:01,440 --> 00:33:09,720 view of let's say the world's ecosystems and biodiversity and creating such a capabilit... 279 00:33:09,720 --> 00:33:17,840 all these 32 programs on wetlands another 10 land cover maps another 10 mangrove... 280 00:33:17,839 --> 00:33:24,599 another I don't know how many of marine coastal areas will actually allow us to... 281 00:33:24,599 --> 00:33:34,199 opportunity for coherence coherence in policy coherence in environmental governance system 282 00:33:34,199 --> 00:33:40,879 coherence in national level decisions coherence in investments so that we can in... 283 00:33:40,880 --> 00:33:48,800 more harmonized standardized reference point for that critical information and we are i... 284 00:33:48,800 --> 00:33:55,440 in the process of developing this very innovative program it's innovative because... 285 00:33:55,440 --> 00:34:01,800 force these partners to collaborate and sometimes there's the best disruption if w... 286 00:34:01,800 --> 00:34:09,320 aim for is getting that robust collaboration but also in the way that will allow us to... 287 00:34:09,320 --> 00:34:16,400 data over areas where data has not been collected because those landmass is so sma... 288 00:34:16,400 --> 00:34:22,000 ocean states the Sentinel missions actually they turn off their sensors when they go o... 289 00:34:22,000 --> 00:34:29,600 areas to conserve energy so we need to communicate with this critical data provid... 290 00:34:29,600 --> 00:34:34,640 populations there on the ground for whom getting access to information is actually ... 291 00:34:34,639 --> 00:34:42,279 survival cultural survival physical survival and so we are really excited to be conveni... 292 00:34:42,279 --> 00:34:48,799 global ecosystem we call it Atlas at the moment but it's probably gonna have a... 293 00:34:48,799 --> 00:34:57,159 program that will give us real insights into the terrestrial freshwater marine ecosyste... 294 00:34:58,159 --> 00:35:08,279 interdependencies in near real time and filling the gaps that currently exist with... 295 00:35:08,279 --> 00:35:14,119 intelligence but also with the participation of the local communities who live in those... 296 00:35:14,119 --> 00:35:20,719 areas and there are something very interesting also comes into play as we use... 297 00:35:20,719 --> 00:35:28,399 we even its development has to be participatory because when we talk about... 298 00:35:28,399 --> 00:35:34,199 live in some of those areas they use very different language than our modern English... 299 00:35:34,199 --> 00:35:41,839 machine learning and artificial intelligence has to be trained with the language which... 300 00:35:41,839 --> 00:35:49,959 in this room speak so to us this program that we are really developing just now and woul... 301 00:35:50,000 --> 00:35:56,400 have the UAE to be one of the first partners here is going to address all of those... 302 00:35:56,400 --> 00:36:05,440 both in data inclusivity and coherence you brought a lot of great points and it's rea... 303 00:36:05,440 --> 00:36:12,199 that we don't have a lot of time but mr. Clint yeah and I mentioned gaps and then... 304 00:36:12,199 --> 00:36:18,840 communication between different partners how can we bridge these gaps like when you hav... 305 00:36:19,000 --> 00:36:23,920 providers and different data users that we need to connect to give us your line of... 306 00:36:23,920 --> 00:36:30,880 minutes I first of all I really appreciate what you had to say Yana it's quite quite... 307 00:36:30,880 --> 00:36:39,320 and I think that's really a great initiative and we would want to be standing in suppor... 308 00:36:39,320 --> 00:36:47,360 as you were talking one of the things I was thinking about is we have an expression al... 309 00:36:47,360 --> 00:36:56,519 us have this expression sunlight is the best disinfected and and part of how how do we... 310 00:36:56,519 --> 00:37:06,640 and enable other parts of the world so that no one gets left behind everybody can can... 311 00:37:06,640 --> 00:37:14,160 to information that really helps them solve their problems part of that is how the res... 312 00:37:14,319 --> 00:37:23,839 educational materials and and promote the idea of learning these approaches that can... 313 00:37:23,839 --> 00:37:31,359 useful to tap into people who have experience you know that kind of deep insight that comes 314 00:37:31,359 --> 00:37:41,519 from tough learning you know to solve tough problems that that should be a part of tha... 315 00:37:41,559 --> 00:37:47,400 of educational perspective the things that that one of the things that I'm really... 316 00:37:47,400 --> 00:37:55,320 about is that that focus on education and it's interesting to me because if you look... 317 00:37:55,320 --> 00:38:02,440 look at our young children they're all growing up as digital natives so they're... 318 00:38:02,440 --> 00:38:09,199 different they have a different approach to thinking about problems than we did when w... 319 00:38:09,679 --> 00:38:17,039 up and they're they're more prepared to take these things on having said that there's... 320 00:38:17,039 --> 00:38:25,839 a certain amount of leadership and and I think kind of kind of deeper education and... 321 00:38:25,839 --> 00:38:32,159 we all have to be aware of that we need to provide thank you so much and brief thanks to 322 00:38:32,159 --> 00:38:37,519 professor and Iana as well for this informative session unfortunately we're ou... 323 00:38:37,519 --> 00:38:40,679 learned a lot and I hope everyone enjoyed this session thank you everyone