軌跡感知的跨視角序列觀測地理定位

摘要

跨視角地理定位旨在將地面觀測與帶有地理標籤的衛星影像進行匹配。近期方法顯示，如影片片段這類序列化查詢能提供比單張影像更豐富的時空線索，然而這些方法忽略了另一種互補的序列化模態：路線描述——它以更高層次的抽象捕捉相同軌跡，並且往往是唯一可用的輸入（例如，用戶引導自動駕駛車輛前往接駁點）。為填補此缺口，我們提出SeqGeo-VL資料集，包含模擬39K組影片-文字-衛星三元組，以及TrajLoc統一框架，能同時處理影片片段與路線描述。透過利用密集視覺語義與抽象語言語義，TrajLoc使這些模態能夠相互強化跨視角匹配。我們進一步提出輕量級模組TrajMod，該模組根據軌跡幾何條件化查詢嵌入，從而產生空間感知表徵。實驗結果顯示，TrajLoc在影片與文字地理定位任務上均顯著優於現有最先進方法。專案頁面可於 https://humblegamer.github.io/trajloc/ 查閱。

English

Cross-view geo-localization matches ground-level observations against geo-tagged satellite imagery. Recent methods show that sequential queries such as video clips yield richer spatiotemporal cues than single images, yet they overlook a complementary sequential modality: route descriptions -- which capture the same trajectory at a higher level of abstraction and are often the only input available (e.g., a user directing an autonomous vehicle to a pickup point). To bridge this gap, we introduce SeqGeo-VL, a dataset of sim39K video-text-satellite triplets, and TrajLoc, a unified framework capable of processing both video clips and route descriptions. By leveraging both dense visual and abstract linguistic semantics, TrajLoc enables these modalities to mutually reinforce cross-view matching. We further propose TrajMod, a lightweight module that conditions query embeddings on trajectory geometry, yielding spatially-aware representations. Experiments show that TrajLoc achieves substantial gains over state-of-the-art methods on both video and text geo-localization. The project page is available at https://humblegamer.github.io/trajloc/.