CompHub?實時聚合多平臺的數據類(Kaggle、天池…)和OJ類(Leetcode、牛客…）比賽。本賬號會推送最新的比賽消息，歡迎關注！

更多比賽信息見?CompHub主頁

---

以下內容摘自比賽主頁（點擊文末閱讀原文進入）

Part1賽題介紹

題目

Large-Scale Fine-Grained Building Classification for Semantic Urban Reconstruction

• Track 1: Building Detection and Roof Type Classification

• Track 2: Multi-Task Learning of Joint Building Extraction and Height Estimation

舉辦平臺

Codalab

背景

Buildings are essential components of urban areas. While research on the extraction and 3D reconstruction of buildings is widely conducted, information on the fine-grained roof types of buildings is usually ignored. This limits the potential of further analysis, e.g., in the context of urban planning applications. The fine-grained classification of building roof type from satellite images is a highly challenging task due to ambiguous visual features within the satellite imagery. The difficulty is further increased by the lack of corresponding fine-grained building classification datasets.

建筑是城市的重要組成部分。在建筑物的提取和三維重建研究廣泛開展的同時，細粒度屋頂類型的信息往往被忽略。這限制了進一步分析的潛力，例如在城市規劃應用的背景下。從衛星圖像中對建筑物屋頂類型進行細粒度分類是一項極具挑戰性的任務，因為衛星圖像中存在模糊的視覺特征。由于缺乏相應的細粒度建筑分類數據集，難度進一步增加。

The 2023 IEEE GRSS Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS), the Aerospace Information Research Institute under the Chinese Academy of Sciences, the Universit?t der Bundeswehr München, and GEOVIS Earth Technology Co., Ltd. aims to push current research on building extraction, classification, and 3D reconstruction towards urban reconstruction with fine-grained semantic information of roof types.

2023年IEEE GRSS數據融合大賽，由IEEE地球科學與遙感學會(GRSS)圖像分析與數據融合技術委員會(IADF TC)、中國科學院航空航天信息研究所、Universit?t der Bundeswehr München和GEOVIS地球技術有限公司組織，旨在推動當前建筑提取、分類、以及基于屋頂類型語義信息的城市重建三維重建。

To this aim, the DFC23 establishes a large-scale, fine-grained, and multi-modal benchmark for the classification of building roof types. It consists of two challenging competition tracks investigating the fusion of optical and SAR data, while focusing on roof type classification and building height estimation, respectively.

為此，DFC23建立了大規模、細粒度、多模式的建筑屋頂類型分類基準。它由兩個具有挑戰性的賽道組成，研究光學和SAR數據的融合，同時分別專注于屋頂類型分類和建筑物高度估計。

Part2時間安排

• Development Phase, Start: Jan. 4, 2023, noon

• Test Phase, Start: March 6, 2023, noon

Part3獎勵機制

• The first, second, third and fourth-ranked teams in each track will be declared as winners.

• The authors of the winning submissions will:

• Present their approach in an invited session dedicated to the DFC23 at IGARSS 2023

• Publish their manuscripts in the Proceedings of IGARSS 2023

• Be awarded IEEE Certificates of Recognition

• The first, second, and third-ranked teams of each track will receive?2,000, and $1,000 (USD), respectively, as a cash prize.

• The authors of the first and second-ranked teams of each track will co-author a journal paper which will summarize the outcome of the DFC23 and will be submitted with open access to IEEE JSTARS.

• Top-ranked teams will be awarded during IGARSS 2023, Pasadena, USA in July 2023.

Part4賽題描述

Track 1: Building Detection and Roof Type Classification

This track focuses on the detection and classification of building roof types from high-resolution satellite optical imagery and SAR images. The SAR and optical modalities are expected to provide complementary information. The given dataset covers senventeen cities worldwide across six continents. The classification task consists of 12 fine-grained, predefined roof types. Figure 1 shows an example.

本賽道的重點是從高分辨率衛星光學圖像和SAR圖像中檢測和分類建筑物屋頂類型。SAR和光學模式有望提供補充信息。給定的數據集涵蓋了全球六大洲的17個城市。分類任務由12種細粒度的預定義屋頂類型組成。圖1顯示了一個示例。

Figure 1: An example image tile of multi-modal data (optical and SAR) for building detection and roof type classification.

Track 2: Multi-Task Learning of Joint Building Extraction and Height Estimation

This track defines the joint task of building extraction and height estimation. Both are two very fundamental and essential tasks for building reconstruction. Same as in Track 1, the input data are multi-modal optical and SAR satellite imagery. Building extraction and height estimation from single-view satellite imagery depend on semantic features extracted from the imagery. Multi-task learning provides a potentially superior solution by reusing features and forming implicit constraints between multiple tasks in comparison to conventional separate implementations. Satellite images are provided with reference data, i.e., building annotations and normalized Digital Surface Models (nDSMs). The participants are required to reconstruct building heights and extract building footprints. Figure 2 shows an example.

本賽道定義了建筑物提取和高度估計的聯合任務。這是建筑改造中兩個非常基本和必要的任務。與賽道1相同，輸入數據為多模態光學和SAR衛星圖像。單視角衛星圖像的建筑物提取和高度估計依賴于從圖像中提取的語義特征。與傳統的單獨實現相比，多任務學習通過重用特性和在多個任務之間形成隱式約束，提供了一種潛在的優秀解決方案。衛星圖像提供了參考數據，即建筑注釋和標準化數字表面模型(ndsm)。參與者需要重建建筑高度和提取建筑足跡。圖2顯示了一個示例。

Figure 2: An example for joint building extraction and height estimation.

---

???????

總結

以上是生活随笔為你收集整理的【数据科学赛】大规模细粒度建筑分类 #图像分类 #建筑分割和高度预估 #$1,6000的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。