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Lots of Earth Observation data has become available at no charge in Europe and the US recently and there is a strong push for more open EO data. With precision farming, advanced agriculture using GPS, satellite observations and tractors with on-board computers, the farming process is performed as accurately and efficiently as possible. This is achieved by combining data from earth observations with other geospatial sources such as cadastral data, data on the quality of the soil, vegetation and protected areas. This enables the farmer to find the optimal trade-off in maximizing his yield with minimal use of fertilizers and pesticides while respecting environmental protection.

Definitely an intersting book about superorganisms (bees, ants, etc) by the two masters Bert Hoelldobler and EO Wilson.

beautiful! anybody knows of similar trials to fool EO data classifiers?

I am not aware of any similar trials, yet this study remind me of generative adversarial talking heads https://arxiv.org/pdf/1803.07716.pdf https://www.youtube.com/watch?v=p1b5aiTrGzY

appears like solid data confirming the link between the two; using also EO data from space

One more reason why we should be much more open about all these EO data we have .... The two citizen scientists, Kian Jek and Robert Gagliano, are listed as authors on the scientific paper recently published. I love this: the digital nature of these data make it far, far easier to analyze the science than it was in the past, and also easier to get the data out to people. Because of this, we have an explosive growth in these kinds of projects. Planet Hunters is great, but then so is Galaxy Zoo, Moon Mappers, Ice Hunters, and so many others. You can find several of these collected at the CosmoQuest website.

Wiktor is also collecting these science games in the ACT-wiki. You should have a look at: http://sophia.estec.esa.int/actwiki/index.php/On-line_Games_4_Science

Not easily digestible piece of esa document, but to prove Paul's point. And yes, they have already planned to train neural networks on a database of different water types, so that the satellite figures out from the combined retrieval of backscattering and absorption = f(λ) which type of water it is looking at. Type of water relates to οptical clarity of the water, a variable called turbidity. We could do this as well for mapping iron fertilization locations if we find its spectral signature. Lab time?????

The Sentinel-2A satellite has been in orbit for only a matter of weeks, but new images of an algal bloom in the Baltic Sea show that it is already exceeding expectations. Built essentially as a land monitoring mission, Sentinel-2 will also certainly find its way into marine applications.

in french - though language not important ... letters visible from space during concert ... 

A hybrid image classification method was employed for this specific purpose by incorporating the advantages of both pixel-based and object-based image classification approaches. This was performed in two steps: firstly, a pixel-based image classification method, i.e., artificial neural network was applied to classify potential targets with similar spectral reflectance at pixel level; and then an object-based image classification method was used to further differentiate animal targets from the surrounding landscapes through the applications of expert knowledge. As a result, the large animals in two pilot study areas were successfully detected with an average count error of 8.2%, omission error of 6.6% and commission error of 13.7%. The results of the study show for the first time that it is feasible to perform automated detection and counting of large wild animals in open savannahs from space

And Paul, it includes neural networks!

I kept telling you guys but you just laughed and laughed :))

nice, did any of you try it already