ArXiv

Finding Change in Satellite Archives from Text: How to Combine Before-and-After Images Efficiently

2026-07-30 · 17:35 UTC ·Simon Roy, Mark Bong, Giovanni Beltrame ·1 min read
Authors
Simon Roy, Mark Bong, Giovanni Beltrame
Categories
cs.CV, cs.IR
arXiv
https://arxiv.org/abs/2607.28571v1
PDF
https://arxiv.org/pdf/2607.28571v1

Brief

Roy et al. perform a controlled comparison of eight fusion-module designs (attention, a state-space model Mamba, and learned compression TBF) for text-driven search over before-and-after satellite image pairs, using a frozen CLIP encoder and one training recipe across two benchmarks (LEVIR-CC, Dubai-CC) with ten random seeds. Key results: a cheap two-stage shortlist+attention pipeline yields 10–15× query-cost savings with equal or better recall; Mamba’s linear scan is limited by memory bandwidth at L=196; and TBF reduces parameters 2.3× and latency 1.6× for a negligible BLEU-1 loss (0.007) though heavy compression can drop change-relevant details.

Why it matters

A training-free two-stage search (cheap difference model to shortlist, attention fusion to re-rank) matches or exceeds full-fusion recall on LEVIR-CC while reducing query cost by 10–15×, and achieves comparable R@1/R@5 on Dubai-CC.

Key details

  • A linear-time state-space scan (Mamba) gives no practical speed benefit at typical ViT patch counts (L = 196) because the scan is memory-bandwidth limited, whereas attention maps efficiently to parallel hardware.
  • Temporal Bottleneck Fusion (TBF) compresses the fused representation, cutting parameters by 2.3× and latency by 1.6× for a change-only BLEU-1 cost of 0.007; however, more aggressive compression discards change-relevant detail not captured by aggregate metrics.
Source evidence

Abstract

Operational Earth observation increasingly calls for answering queries such as find the image pairs where a new building appeared.'' This means searching an archive of before-and-after (bi-temporal) satellite image pairs and ranking each pair by how well it matches a natural-language description of the change. The component that performs this match, the fusion module that combines thebefore'' and ``after'' views, must be run at query time across many candidate pairs, so its speed largely sets the cost of every search. We present a controlled comparison of how to build that module. Using one fixed image encoder (a frozen CLIP model) and one training recipe for all variants, we evaluate eight designs drawn from three families: attention, state-space models (Mamba), and learned compression (our Temporal Bottleneck Fusion, TBF). Each design is tested on two benchmarks (LEVIR-CC and Dubai-CC) with ten random seeds, so the reported differences are statistically grounded. We outline three findings: first, a training-free two-stage search (a cheap difference model that shortlists candidates, followed by attention fusion that re-ranks them) matches or exceeds full-fusion recall on LEVIR-CC while cutting query cost $10$-$15\times$, with comparable R@1/R@5 on Dubai-CC; second, the linear-time scan of Mamba, attractive on paper, gives no speed benefit at the patch counts typical of vision transformers ($L{=}196$): the scan is limited by memory bandwidth, whereas attention maps cleanly onto parallel hardware; and third, compressing the fused representation (TBF) reduces parameters by $2.3\times$ and latency by $1.6\times$ for a change-only BLEU-1 cost of $0.007$, although more aggressive compression quietly discards change-relevant detail that aggregate metrics fail to reveal.

Comment: 10 pages, 3 figures