Constructing a Large-scale Landslide Database Across Heterogeneous
Environments Using Task-Specific Model Updates
Abstract
In this article, we consider the scenario where remotely sensed images
are collected sequentially in temporal batches, where each batch focuses
on images from a particular ecoregion, but different batches can focus
on different ecoregions with distinct landscape characteristics. For
such a scenario, we study the following questions: (1) How well do DL
models trained in homogeneous regions perform when they are transferred
to different ecoregions, (2) Does increasing the spatial coverage in the
data improve model performance in a given ecoregion (even when the extra
data do not come from the ecoregion), and (3) Can a landslide pixel
labelling model be incrementally updated with new data, but without
access to the old data and without losing performance on the old data
(so that researchers can share models obtained from proprietary
datasets)? We address these questions by a framework called
Task-Specific Model Updates (TSMU). The goal of this framework is to
continually update a (landslide) semantic segmentation model with data
from new ecoregions without having to revisit data from old ecoregions
and without losing performance on them. We conduct extensive experiments
on four ecoregions in the United States to address the above questions
and establish that data from other ecoregions can help improve the
model’s performance on the original ecoregion. In other words, if one
has an ecoregion of interest, one could still collect data both inside
and outside that region to improve model performance on the ecoregion of
interest. Furthermore, if one has many ecoregions of interest, data from
all of them are needed.