---
title: 'DescribeEarth: Fine-Grained Image Captioning'
url: https://www.emergentmind.com/topics/describeearth
type: topic
---

# DescribeEarth: Fine-Grained Image Captioning

DescribeEarth is a multimodal large language model framework for **fine-grained, object-centric language generation in remote sensing imagery**, introduced together with the task of **Geo-DLC**, the **DE-Dataset**, and the **DE-Benchmark** [2509.25654]. Its stated purpose is to move remote sensing captioning beyond image-level scene summary toward **object-level fine-grained image captioning**, where a model receives an image and a user-specified region of interest and produces a detailed description of a localized target such as an airport, harbor, damaged building, ship, pylon, or other object embedded in a large overhead image.

## 1. Geo-DLC as a task formulation

The paper defines **Geo-DLC**, short for **geospatial detailed localized captioning**, as a task in which an image \(I\) and a user-specified region of interest \(R\) are mapped to a detailed textual description \(T\):
\[
\begin{aligned}
T = Model(I, R)
\label{eq:task_define}
\end{aligned}
\]
The region \(R\) may come from a bounding box, point, mask, or interactive query, but the expected output goes beyond naming the object. It is intended to describe **intrinsic object features, spatial features, and contextual environmental attributes** in a coherent way [2509.25654].

The motivation is tied to the properties of

Source: https://www.emergentmind.com/topics/describeearth