Well, I am honoured that @aninda_bitm requested me to share. Yes, I will gladly share my approach, but not my entire solution. It is a bit early to give too much details, but I will start sharing some of the important aspects of my solution here. It will be non-technical on purpose. I hope you like it like that. Over the coming weeks, especially as the leaderboard settles, I will share more bits and pieces.

Note that there was a similar competition a year ago (spot-the-crop) and there our solution won in both tracks (using S1 and S2 and just S2 imagery). That solution was the starting point. So to start, I have to go back to that one. Normally, for something like this, you start by calculating some statistics for each field. So you would calculate e.g. the mean, standard deviation, skewness and kurtosis for each band of each field and use something like a gradient booster to try and classify based on that.

In the previous competition, after a lot of testing, I realised that, for that one, the higher moments, specifically skewness and kurtosis, does not really contribute. So that (spot-the-crop) solution used only the mean and standard deviation to classify crop types.

This time around, given the small size of fields, I focussed on just the measures of locality (mean, mode, median and others). I did not even use the standard deviation or other measures of spread (much). Now keep in mind that I did not test it this time, I simply made the assumption, but that allowed me to save a lot of time and just focus on models and ways to use measures of locality to classify fields.

For what it is worth, I really struggled with this one. For a long time I was stuck on a score of 1.6+ and I had to work very hard to break through that. So while I share what I did here, I do think there are better ways to approach this.

I have a nice graph that sort of illustrates my progress which I will share later, but my progression was something like

• quick model that I believe can work but very stuck at 1.6+
• start to calibrate model, now stuck at 1.5+
• add some more nifty features (mostly from my team mate @Moto) ~ 1.5
• huge effort to improve model 1.4+
• breakthrough idea 1.25
• more refinements 1.21
• finally another breakthrough idea on last day of competition! 1.20

and that was it. Now each of these bullets represent a lot of subs and a lot of hard work. I'll share details on some of these in the coming days. This sort of shows you how much you get from perspiration and how much from inspiration, but of course you need both in a competition like this.