I followed this remotely via Twitter (don't know whether other remote channels were available). My notes on this sit at https://github.com/Daniel-Mietchen/events/issues/508 .

Microsoft hololens is designed to enable a new dimension of future productivity with the introduction of this self-contained holographic tools. The tool allows for engagement in holograms in the world around you.

Learning environments will gain ground with the implementation of this future tool in the learning program and models.

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Not sure this is a wide-ranging trend.

According to a recent survey, the younger generation prefer working from the office to remote working, unlike baby boomers, who would rather work from home.

The list of remote jobs is fantastic.

This difficulty in comparing algorithms (due to differences in forest type, location, and assessment strategy used for different algorithms) indicates a clear need for set of open data and centralized assessment to allow different methods to be competed against one another to determine the best routes forward.

This kind of approach has been very successful in other image analysis problems (e.g., ImageNET).

The National Ecological Observatory Network data seems ideal for doing something like this. Data is/will be available for a variety of different systems and with LiDAR, Hyperspectral, RGB, and field data for large numbers of plots.

This difficulty of applying across forest types is central to the challenges of developing approaches that can be applied to continental scale data collection like that being conducted by NEON. Overcoming this challenge will likely require incorporating ecological information into models, not just the remote sensing, and determining how to choose and adjust different approaches to get the best delineations possible based on information about the forest type/location.

This kind of data is available for NEON plots and so these methods could potentially be well leveraged with NEON data. This would be particularly true if the NEON plot data could be used to develop a spatial model for these features that could be used to predict their values across space.

Only a small fraction of studies combine LiDAR and Hyperspectral data for the crown delineation phase of analysis.

Excellent point about the importance of integrating all available data to make the best possible crown delineations. In the case of the National Ecological Observatory Network Airborne Observation Platform methods that leverage the LiDAR, hyperspectral, and high-resolution RGB photographic data should have the potential to outperform methods that ignore components of this data.

Tips for remote workers and remote-friendly companies.<br> Andy Mention https://twitter.com/amention<br> http://andymention.com/remote-tooling/

More detailed data was required to supplement the MODIS based remote sensing to fully understand how it could be used for quantifying fire.

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