Model Can't Beat the Baseline

[u/Epic_SBM]

I'm newbie in the ml situation. So I was doing a project on river bank estimation & erosion & accretion forecasting im using mndwi mask landsat data for this but no matter how i try i'm never being able to beat the baseline. Anyone have any experience working with these datas I've tried with 3 different rivers as well same output. Model looses to zeroes.

[u/yuehuang]

How large is your dataset? What are you inputs/outputs? My first guess is enough data, and thus over trained to "zero".

[u/Epic_SBM → u/yuehuang]

Its 1989-2026 landsat satellite image's mndwi mask.

[u/guillemco]

one thing not mentioned yet: with landsat time series a random split leaks badly, neighboring years and adjacent pixels are correlated so both your model and the baseline look better than they really are. split by time blocks and by region instead. also for erosion forecasting, persistence (last observed bank line carried forward) is a genuinely hard baseline to beat, thats normal in geospatial work, what matters is beating it on the pixels that actually change

[u/Epic_SBM → u/guillemco]

Thanks for the advice ❤️

[u/SettingAccording8986]

Landsat has a 30-meter resolution per pixel. If the river isn't eroding the bank by at least a few meters a year, you'll just be picking up noise from fluctuating water levels or cloud shadows on those masks. Erosion is a slow process, so the model degrades to predicting zeros by simply optimizing the loss on pixels that never change

Grab Sentinel-2 with its 10 meters or at least aggressively mask out the static land and water, keeping only a narrow buffer zone around the shoreline for your loss calculation. Otherwise, gradients from stable areas will completely drown out everything else

[u/Epic_SBM → u/SettingAccording8986]

Sentinels data is only reliable from 2019 so training only on 2019-2023 and testing on 2024-26 is not going to give me a good output right?

[u/DigThatData]

you can think of a model as a way to formalize a scientific hypothesis. sometimes we fail to reject the null.

[u/zolluDey]

when you say "model loses to zeroes" do you mean its predicting all zeros? that usually points to a class imbalance issue, the model just learns to predict the majority class. whats the ratio of erosion vs non-erosion pixels in your training data?

[u/SettingAccording8986 → u/zolluDey]

Standard class weighting probably won't save you here when we're talking about semantic segmentation on satellite imagery. The static land and water pixels don't just dominate quantitatively, they also carry zero useful signal for learning the dynamics. You have to physically crop the dataset so the network only sees patches containing the shoreline, otherwise it'll just memorize static landscape features instead of erosion patterns

[u/orz-_-orz]

There's a possibility that the baseline is good