Contrasting Local and Global Modeling with Machine Learning and Satellite Data: A Case Study Estimating Tree Canopy Height in African Savannas

Esther Rolf, Lucia Gordon, Milind Tambe, Andrew Davies

Abstract:

While advances in machine learning with satellite imagery (SatML) are facilitating envi-
ronmental monitoring at a global scale, developing SatML models that are accurate and
useful for local regions remains critical to understanding and acting on an ever-changing
planet. As increasing attention and resources are being devoted to training SatML models
with global data, it is important to understand when improvements in global models will
make it easier to train or fine-tune models that are accurate in specific regions. To explore
this question, we design the first study that explicitly contrasts local and global training
paradigms for SatML, through a case study of tree canopy height (TCH) mapping in the
Karingani Game Reserve, Mozambique. We find that recent advances in global TCH
mapping do not necessarily translate to better local modeling abilities in our study region.
Specifically, small models trained only with locally-collected data outperform published
global TCH maps, and even outperform globally pretrained models that we fine-tune using
local data. Analyzing these results further, we identify specific points of conflict and syn-
ergy between local and global modeling paradigms that can inform future research toward
aligning local and global performance objectives in geospatial machine learning.