Reference Data Generation for Remote Sensing Image Classification: A Comprehensive Review of Methods, Challenges, and Recent Advances

Torun A. T., Özdarici Ok A.

REMOTE SENSING, cilt.18, sa.20, ss.1-39, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 18 Sayı: 20
  • Basım Tarihi: 2026
  • Dergi Adı: REMOTE SENSING
  • Derginin Tarandığı İndeksler: Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Earth, Atmospheric, & Aquatic Science Collection (ProQuest), Engineering Source (EBSCO), Scopus, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest), Aerospace Database, Science Citation Index Expanded (SCI-EXPANDED), Compendex, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.1-39
  • Ankara Hacı Bayram Veli Üniversitesi Adresli: Evet

Özet

Image classification is a fundamental technique that has been broadly adopted in remote

sensing, computer vision and related areas. High-quality annotated datasets are necessary

for the development and reliable evaluation of classification and segmentation algorithms

for accurate thematic mapping. Reference data are critical in this sense, as their quality

has a direct impact on the performance, accuracy and generalization of the model. However,

generating reliable reference data remains challenging due to methodological, technical,

and annotation-related uncertainties. This study reviews the principal challenges in

reference data generation, and the solutions proposed in scientific literature. A total of 100

papers indexed in the Web of Science were examined. The review covers traditional reference

data generation methods as well as emerging approaches based on semi-supervised,

self-supervised, and active learning, segmentation- and post-processing-assisted

labeling, benchmark dataset construction, sampling and accuracy assessment, and deep

learning and automated label generation. Major challenges and emerging research directions

are also discussed. The results indicate a clear shift from traditional manual techniques

to more automated, learning based and accuracy driven methods. The study provides

a framework of existing techniques and future research areas in image classification.