CLIP-RSICD: Um Vision Transformer com Ajuste Fino para Classificação da Cobertura do Solo Urbano
DOI:
https://doi.org/10.5433/1679-0375.2026.v47.54490Palavras-chave:
mapeamento espacial, modelos visão-linguagem, morfologia urbana, suporte à decisãoResumo
O mapeamento preciso das categorias de cobertura do solo urbano (CSU), com elevado detalhamento espacial e temático, é fundamental para o planejamento territorial baseado em evidências. Contudo, a generalização dos modelos de classificação é limitada pelas variações regionais da morfologia urbana. Esse cenário é marcante nos centros urbanos brasileiros, onde a expans o contínua reconfigura dinâmicas metropolitanas e intraurbanas, exigindo o monitoramento detalhado do solo para subsidiar o zoneamento, a distribuição de infraestrutura e a proteção ambiental. Diante disso, avalia-se o uso do codificador visual do modelo Contrastive Language–Image Pre-Training (CLIP), adaptado com imagens de sensoriamento remoto (SR), para a categorização multiclasse da CSU. O esquema taxonômico adota dez classes, abrangendo setores edificado de alta, média e baixa densidade, áreas industriais, áreas vegetadas e solo exposto. Os produtos de CSU de alta resolução (AR) constituem uma base empírica sólida para a caracterização morfológica e a análise aplicada ao planejamento em cidades de porte médio. A partir de imagens de SR {de AR} e livre acesso, a metodologia foi aplicada ao núcleo urbano central da Região Metropolitana de Londrina, Sul do Brasil. O~modelo CLIP alcançou acurácia global de 93%, superando o desempenho de nove arquiteturas convencionais de aprendizado profundo. Os resultados evidenciam que codificadores pré-treinados em linguagem-visão conseguem extrair representações discriminativas de imagens de SR, configurando-se como uma ferramenta escalável para a análise territorial.
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